Methods for computing rolling analyte measurement values, microprocessors comprising programming to control performance of the methods, and analyte monitoring devices employing the methods

ABSTRACT

The present invention relates to methods to increase the number of analyte-related signals used to provide analyte measurement values, e.g., when two or more analyte-related signals are used to obtain a single analyte measurement value a “rolling” value based on the two or more signals can be employed. In another aspect, interpolation and/or extrapolation methods are used to estimate unusable, missing or error-associated analyte-related signals. Further, interpolation and extrapolation of values are employed in another aspect of the invention that reduces the incident of failed calibrations. Further, the invention relates to methods, which employ gradients and/or predictive algorithms, to provide an alert related to analyte values exceeding predetermined thresholds. The invention includes the above-described methods, one or more microprocessors programmed to execute the methods, one or more microprocessors programmed to execute the methods and control at least one sensing and/or sampling device, and monitoring systems employing the methods described herein.

CROSS-REFERENCE TO RELATED APPLICATION

This application is related to U.S. Provisional Patent Applications Ser.Nos. 60/300,511, filed 22, Jun. 2001, and 60/342,297, filed 20, Dec.2001, from which priority is claimed under 35 USC §119(e)(1), and whichapplications are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present invention includes, but is not limited to, methods forimproving the performance of an analyte monitoring system that providesa series of analyte-related signals over time, one or moremicroprocessors programmed to execute the methods, one or moremicroprocessors programmed to execute the methods and control a sensingdevice, one or more microprocessors programmed to execute the methods,control a sensing device, and control a sampling device, and monitoringsystems employing the methods of the present invention. In oneembodiment, the methods relate to glucose monitoring systems. In oneaspect of the present invention, a rolling value is employed usingsignal data provided by an analyte sensor. The rolling value method ofthe present invention provides for more frequent updating and reportingof analyte measurement values. Another aspect of the present inventionis employing interpolation and/or extrapolation methods to providemissing or error-associated signals in a series of analyte-relatedsignals. Another aspect of the invention relates to methods of providingan alert related to analyte values exceeding predetermined thresholds(e.g., high and/or low thresholds) or ranges of values. In this aspectof the invention a gradient method and/or predictive algorithm methodmay be used. Yet another aspect of the present invention is a method forprocessing data from an analyte monitoring system that reduces theincidence of failed calibrations. The present invention includes, but isnot limited to, methods, microprocessors programmed to execute themethods, and monitoring systems (comprising, for example, a samplingdevice, a sensing device, and one or more microprocessors programmed tocontrol, for example, (i) a measurement cycle utilizing the sampling andsensing devices, and (ii) data gathering and data processing related tothe methods of the present invention).

BACKGROUND OF THE INVENTION

Numerous systems for monitoring analyte (e.g., glucose) amount orconcentration in a subject are known in the art, including, but notlimited to the following: U.S. Pat. Nos. 5,362,307, 5,279,543,5,695,623; 5,713,353; 5,730,714; 5,791,344; 5,840,020; 5,995,860;6,026,314; 6,044,285; 6,113,537; 6,188,648, 6,326,160, 6,309,351,6,299,578, 6,298,254, 6,284,126, 6,272,364, 6,233,471, 6,201,979,6,180,416, 6,144,869, 6,141,573, 6,139,718, 6,023,629, 5,989,409,5,954,685, 5,827,183, 5,771,890, and 5,735,273.

Self monitoring of blood glucose (BG) is a critical part of managingdiabetes. However, most procedures for obtaining such information areinvasive, painful and provide only periodic measurements. Results fromthe Diabetes Control and Complication Trial Research Group, (TheDiabetes Control and Complication Trial Research Group. N Engl J Med.1993;329:997–1036), UK Prospective Diabetes Study (UK ProspectiveDiabetes Study (UKPDS) Group. Lancet. 1998;352:837–853), and Kumamototrials (Ohkubo Y, Kishikawa H, Araki E, et al. Diabetes Res Clin Pract.1995;28:103–117) showed that a tight glucose control regiment, whichuses frequent glucose measurements to guide the administration ofinsulin or oral hypoglycemic agents, leads to a substantial decrease inthe long-term complications of diabetes; however, there was a 3-foldincrease in hypoglycemic events (The Diabetes Control and ComplicationTrial Research Group. N Engl J Med. 1993;329:997–1036.). Moreover, asmany as 7 BG measurements per day were not sufficient to detect a numberof severe hypoglycemic and hypoglycemic events (Ohkubo Y, Kishikawa H,Araki E, et al. Diabetes Res Clin Pract. 1995;28:103–117.).

The GlucoWatch® (Cygnus, Inc., Redwood City, Calif.) biographer providesa means to obtain painless, automatic, frequent and noninvasive glucosemeasurements (see, for example, U.S. Pat. Nos. 6,326,160, 6,309,351,6,299,578, 6,298,254, 6,284,126, 6,272,364, 6,233,471, 6,201,979,6,180,416, 6,144,869, 6,141,573, 6,139,718, 6,023,629, 5,989,409,5,954,685, 5,827,183, 5,771,890, and 5,735,273). The device provides upto 3 readings per hour for as long as 12 hours after a single BGmeasurement for calibration (Tamada, et al., JAMA 282:1839–1844, 1999).

Such a monitoring system, which gives automatic and frequentmeasurement, supplies detailed information on glucose patterns andtrends that might identify opportunities for improved BG control.Automatic readings also provide the opportunity for an alarm to besounded in response to values below a user-selected alert level or as aresult of rapid declines in the measured glucose values. Such alarmsprovide a method to reduce the risks of hypoglycemia and make intensivetherapy for persons with diabetes safer and acceptable to more patients.

Further, such monitoring systems can be used to measure an amount orconcentration, in a subject, of one or more analytes, where the one ormore analytes may be in addition to or other than glucose (see, e.g., WO96/00109, published 4, Jan. 1996.

The present invention offers methods of improving performance of analytemonitoring systems that supply a series of analyte-related signals overtime, for example, the GlucoWatch biographer.

SUMMARY OF THE INVENTION

The present invention includes, but is not limited to, methods forimproving the performance of an analyte monitoring system that providesa series of analyte-related signals over time, one or moremicroprocessors programmed to execute the methods, one or moremicroprocessors programmed to execute the methods and control a sensingdevice, one or more microprocessors programmed to execute the methods,control a sensing device, and control a sampling device, and monitoringsystems employing the methods (comprising, for example, a samplingdevice, a sensing device, and one or more microprocessors programmed tocontrol, for example, (i) a measurement cycle utilizing the sampling andsensing devices, and (ii) data gathering and data processing related tothe methods of the present invention).

In a first aspect, the present invention relates to methods forcalculating a series of average signals wherein (i) each average signalis calculated based on two or more contiguous (i.e., next to or near intime or sequence) signals in the series, and (ii) each average signalprovides a measurement related to the amount or concentration of analytein the subject; or, alternately calculating a series of sums, wherein(i) each summed signal is calculated based on two or more contiguous(i.e., next to or near in time or sequence) signals in the series, and(ii) each summed signal provides a measurement related to the amount orconcentration of analyte in the subject. In this aspect the inventionalso comprises one or more microprocessors programmed to execute themethods, and monitoring systems employing the methods.

This aspect of the present invention is used, for example, in a methodfor monitoring an amount or concentration of analyte present in asubject, said method comprising:

providing a series of signals over time wherein each signal is relatedto the analyte amount or concentration in the subject; and

calculating a series of average signals wherein (i) each average signalis calculated based on two or more contiguous (i.e., next to or near intime or sequence) signals in the series, and (ii) each average signalprovides a measurement related to the amount or concentration of analytein the subject; or calculating a series of sums, wherein (i) each summedsignal is calculated based on two or more contiguous (i.e., next to ornear in time or sequence) signals in the series, and (ii) each summedsignal provides a measurement related to the amount or concentration ofanalyte in the subject. Missing signals in the series may be estimatedusing interpolation and/or extrapolation, and such estimated signals canbe used in said calculations.

In this aspect, the present invention relates to methods of increasingthe number of analyte measurement values related to the amount orconcentration of an analyte in a subject as measured using an analytemonitoring device. In this method a series of analyte-related signals isobtained from the analyte monitoring device over time. Typically, two ormore contiguous analyte-related signals are used to obtain a singleanalyte measurement value (M). In this method, paired analyte-relatedsignals are typically used to calculate the measurement value. Oneimprovement provided by the present method is that, prior to the presentmethod, such an analyte monitoring device typically used paired signalsto obtain a single measurement value; but an analyte-related signal fromthe monitoring device was not typically used to calculate more than oneanalyte measurement value. In the present method, the two or morecontiguous analyte-related signals, used to obtain the single analytemeasurement value, comprise first and last analyte-related signals ofthe series.

The method involves mathematically computing rolling analyte measurementvalues, wherein (i) each rolling analyte measurement value is calculatedbased on two or more contiguous analyte-related signals from the seriesof analyte-related signals obtained from the analyte monitoring device.Subsequent rolling analyte measurement values are mathematicallycomputed by dropping the first analyte-related signal from the previousrolling analyte measurement value and including an analyte-relatedsignal contiguous and subsequent to the last analyte-related signal usedto calculate the previous rolling analyte measurement value. Furtherrolling analyte measurement values are obtained by repeating thedropping of the first analyte-related signal used to calculate theprevious rolling analyte measurement and including an analyte-relatedsignal contiguous and subsequent to the last analyte-related signal usedto calculate the previous rolling analyte measurement. Each rollinganalyte measurement value provides a measurement related to the amountor concentration of analyte in the subject. By employing this method thenumber of analyte measurement values, derived from the analyte-relatedsignals in the series of analyte-related signals obtained from theanalyte monitoring device, is increased by serially calculating rollinganalyte measurement values.

In one embodiment of this aspect of the invention, the rolling analytemeasurement value is, for example, an average of two or moreanalyte-related signals; alternately, the rolling analyte measurementvalue is a sum of two or more analyte-related signals. In anotherembodiment, each analyte-related signal is represented by an integralover time, and the rolling analyte measurement value is obtained byintegral splitting.

The above method may be practiced, for example, using a monitoringdevice comprising a sampling device and a sensing device, wherein theseries of analyte-related signals obtained from an analyte monitoringdevice is obtained as follows. Samples are extracted from the subjectalternately into a first collection reservoir and then into a secondcollection reservoir using the sampling device, wherein (i) each samplecomprises the analyte, and (ii) the sampling device comprises the firstand second collection reservoirs. The analyte is sensed in eachextracted sample to obtain a signal from each sample that is related tothe analyte amount or concentration in the subject, thus providing aseries of analyte-related signals. The sensing device may, for example,comprise first and second sensors, wherein the first sensor is inoperative contact with the first collection reservoir and the sensingprovides signal S^(A) _(j) (where S^(A) is the signal from sensor A, jis the time interval), the second sensor is in operative contact withthe second collection reservoir and the sensing provides signal S^(B)_(j+1) (where S^(B) is the signal from sensor B, j+1 is the timeinterval), and an analyte measurement value is obtained usinganalyte-related signal from sensor A and sensor B. In this situation,the series of rolling analyte measurement values may be calculatedemploying the following equations:(average signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j))/2,  Eqn. 1(average signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1))/2;  Eqn. 2(average signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2))/2; etc.,  Eqn. 3

wherein (i) (j−1) is the measurement half-cycle previous to j, and (j+2)is two measurement half-cycles after j, and (ii) each average signalcorresponds to a rolling analyte measurement value.

Alternately, the series of rolling analyte measurement values may becalculated using the following equations:(summed signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j));  Eqn. 4(summed signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1)); and  Eqn. 5(summed signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2)); etc.  Eqn. 6

where (j−1) is the measurement half-cycle previous to j, and (j+2) istwo measurement half-cycles after j; and (ii) each summed signalcorresponds to a rolling analyte measurement value.

In one embodiment of this method, a missing or error-associated signalin the series of analyte-related signals obtained from the analytemonitoring device is estimated using interpolation before mathematicallycomputing rolling analyte measurement values. Such missing orerror-associated signals may also be estimated using extrapolationbefore mathematically computing rolling analyte measurement values.

In a preferred embodiment, the analyte is glucose. In one embodiment,the analyte monitoring device comprises (i) an iontophoretic samplingdevice, and (ii) an electrochemical sensing device. The analyte-relatedsignal may, for example, be a current or a charge related to analyteamount or concentration of analyte in the subject.

One or more microprocessors may be utilized to mathematically computerolling analyte measurement values employing the methods describedherein. Further, such one or more microprocessors may be used to controloperation of the components of the analyte-monitoring system (e.g., asampling device and a sensing device of the monitoring system). Inaddition, the one or more microprocessors may control operation of othercomponents, further algorithms, calculations, and/or the providing ofalerts to a subject (user of the analyte-monitoring system). In thisembodiment of the present invention one or more microprocessors may beutilized to execute the method as well as to control components of ananalyte-monitoring system, for example, control obtaining samples andsensing analyte concentration in each obtained sample to provide aseries of signals.

The present invention also includes analyte-monitoring devices employingthe above methods.

In a second aspect the present invention comprises methods ofinterpolation and/or extrapolation to provide missing signals, where aseries of signals is provided by an analyte monitoring system. Such ananalyte monitoring system may comprise one or more sensors that providethe series of signals.

In one embodiment, the present invention includes the use ofrelationships between the signals obtained from different sensors toperform interpolation and/or extrapolation of estimated values. Forexample, in a two sensor system a ratio of signals obtained from a firstsensor relative to a second sensor may be employed in suchinterpolations and/or extrapolations to estimate signal values.

One embodiment of this second aspect of the present invention includes amethod of replacing unusable analyte-related signals when employing ananalyte monitoring device to measure an analyte amount or concentrationin a subject. A series of analyte-related signals, obtained from theanalyte monitoring device over time, is provided wherein eachanalyte-related signal is related to the amount or concentration ofanalyte in the subject. An unusable analyte-related signal is replacedwith an estimated signal, for example, by either:

(A) if one or more analyte-related signals previous to the unusableanalyte-related signal and one or more analyte-related signalssubsequent to the unusable analyte related signal are available, theninterpolation is used to estimate the unusable, interveninganalyte-related signal; or

(B) if two or more analyte-related signals previous to the unusableanalyte-related signal are available, then extrapolation is used toestimate the unusable, subsequent analyte-related signal.

In this method, the analyte monitoring device may comprise one or moresensor devices and a relationship between the signals obtained from thedifferent sensor devices is used in interpolation and/or extrapolationcalculation of estimated values. In one embodiment, the sensor devicecomprises two sensor elements and a ratio of signals obtained from afirst sensor relative to a second sensor is employed in interpolationand/or extrapolation calculation of estimated signal values. Forexample, the analyte monitoring device may comprises a sampling deviceand a sensing device, wherein providing the series of analyte-relatedsignals obtained from an analyte monitoring device comprises:

extracting a sample from the subject alternately into a first collectionreservoir and then into a second collection reservoir using the samplingdevice, wherein (i) each sample comprises the analyte, and (ii) thesampling device comprises the first and second collection reservoirs;and

sensing the analyte in each extracted sample to obtain a signal fromeach sample that is related to the analyte amount or concentration inthe subject, thus providing a series of analyte-related signals, thesensing device comprising first and second sensors, wherein the firstsensor is in operative contact with the first collection reservoir andthe sensing provides signal S^(A) _(j) (where S^(A) is the signal fromsensor A, j is the time interval), the second sensor is in operativecontact with the second collection reservoir and the sensing providessignal S^(B) _(j+1) (where S^(B) is the signal from sensor B, j+1 is thetime interval), and an analyte measurement value is obtained usinganalyte-related signal from sensor A and sensor B.

A relationship between the signals obtained from different sensors maybe used in interpolation and/or extrapolation calculation of estimatedvalues. For example, the relationship between the signals from thedifferent sensors may take the form of a smoothed ratio:R _(i) ^(s) =wR _(i)+(1−W)R _(i−1) ^(s)  Eqn. 10

wherein, for example, R_(i) is the A/B or B/A signal ratio for a i^(th)measurement cycle, R^(S) _(i) is smoothed R for a i^(th) measurementcycle, and w is a smoothing factor and is represented by a fractionbetween and inclusive of 0 through 1, and R^(S) _(i−1) is a smoothedratio for the (i−1)^(th) measurement cycle, wherein the i^(th)measurement cycle is composed of first and second half-cycles and thesecond half-cycle value of the i^(th) measurement cycle precedes S_(j).In one embodiment, wherein a smoothed A/B ratio and a smoothed B/A ratioare employed, and the ratios are as follows: $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\mspace{14mu}\text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\mspace{14mu}\text{9B}}\end{matrix}$

wherein (A/B)_(s,i)and (B/A)_(s,i)refer to “smoothed” AB ratios formeasurement cycle i, (A/B)_(i) and (B/A)_(i), refer to the AB ratio formeasurement cycle i, and (A/B)_(s,i−1) and (B/A)_(s,i−1) refer to thesmoothed AB ratio from the previous measurement cycle i−1.

For interpolation in the situation where both S_(j) and S_(j+2) aresignals from the B sensor (S^(B) _(j) and S^(B) _(j+2)), and S_(j+1) isbeing estimated for the A sensor signal (S^(AE) _(j+1)), interpolationEqn. 7A may be employed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\mspace{14mu}\text{7A}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.

For interpolation in the situation where both S_(j) and S_(j+2) aresignals from the A sensor (S^(A) _(j) and S^(A) _(j+2)), and S_(j+1) isbeing estimated for the B sensor signal (S^(BE) _(j+1)), interpolationEqn. 7C may be employed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\mspace{14mu}\text{7C}}\end{matrix}$

wherein t_(j), is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.

For extrapolation in the situation where S_(j) is signal from sensor A(S^(A) _(j)) and S_(j+1) is signal from B sensor (S^(B) _(j+1)), andS_(j+2) is being estimated for the A sensor signal (S^(AE) _(j+2)),extrapolation Eqn. 8A may be employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\}\frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\mspace{14mu}\text{8A}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.

For extrapolation in the situation where S_(j) is signal from the Bsensor (S^(B) _(j)) and S_(j+1) is signal from the A sensor (S^(A)_(j+1)), and S_(j+2) is being estimated for the B sensor signal (S^(BE)_(j+2)), extrapolation Eqn. 8C may be employed as follows:$\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\}\frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\mspace{14mu}\text{8C}}\end{matrix}$

wherein t_(j) is a measurement half-cycle, t_(j+1), one subsequenthalf-cycle, and t_(j+2) two subsequent half-cycles.

In one embodiment the analyte is glucose. The analyte monitoring devicemay, for example, comprise (i) an iontophoretic sampling device, and(ii) an electrochemical sensing device. The analyte-related signal maybe, e.g., a current or a charge related to analyte amount orconcentration of analyte in the subject.

One or more microprocessors may be utilized to mathematically computeestimated signals employing the methods described herein. Further, suchone or more microprocessors may be used to control operation of thecomponents of the analyte monitoring system (e.g., a sampling device anda sensing device of the monitoring system). In addition, the one or moremicroprocessors may control operation of other components, furtheralgorithms, calculations, and/or the providing of alerts to a subject(user of the analyte monitoring system).

The present invention also includes analyte monitoring devices employingthe above methods.

In a third aspect, the present invention relates to a method forreducing the incidence of failed calibration for an analyte monitoringsystem that is used to monitor an amount or concentration of analytepresent in a subject, where the monitoring system provides a series ofsignals or measurement values. For example, in one embodiment, themethod comprises:

extracting a series of samples from the subject using a sampling device,said extracting alternately into a first collection reservoir and theninto a second collection reservoir, wherein (1) each sample comprisesthe analyte, and (2) said sampling device comprises said first andsecond collection reservoirs;

sensing the analyte in each extracted sample to obtain a signal fromeach sample that is related to the analyte amount or concentration inthe subject, thus providing a series of signals, said sensing devicecomprising a first sensor (A) and second sensor (B), wherein (1) saidfirst sensor (A) is in operative contact with said first collectionreservoir and said second sensor (B) is in operative contact with saidsecond collection reservoir, (2) two consecutive signals comprise ameasurement cycle, and each of the two consecutive signals is half-cyclesignal; and

performing a calibration method to relate analyte amount orconcentration in the subject to signals obtained from the sensors, saidcalibration method comprising:

(i) obtaining a first half-cycle signal S_(j), where a half-cycle signalS_(j+1), or an estimate thereof, and a half-cycle signal S_(j+2), or anestimate thereof, are both used in the calibration method so that thesensor signals correlate to the analyte amount or concentration in thesubject, wherein the calibration method also employs an analytecalibration value that is independently determined;

(ii) providing the analyte calibration value;

(iii) selecting a conditional statement selected from the groupconsisting of:

(a) if neither the second half-cycle signal S_(j+1) nor the thirdhalf-cycle signal S_(j+2) comprise errors, then S_(j+1) and S_(j+2) areused in the calibration method;

(b) if only the second half-cycle signal S_(j+1) comprises an error,then an estimated signal S^(E) _(j+1) is obtained by determining aninterpolated value using signal S_(j) and S_(j+2), wherein saidinterpolated value is S^(E) _(j+1), and S^(E) _(j+1) and S_(j+2) areused in the calibration method;

(c) if only the third half-cycle signal S_(j+2) comprises an error, thenan estimated signal S^(E) _(j+2) is obtained by determining anextrapolated value using signal S_(j) and S_(j+1), wherein saidextrapolated value is S^(E) _(j+2), and S_(j+1) and S^(E) ₁₊₂ are usedin the calibration method; and

(d) if both the second half-cycle signal S_(j+1) and the thirdhalf-cycle signal S_(j+2) comprise errors, then return to (i) to obtaina new half-cycle signal S_(j) from a later measurement half-cycle thanthe first half-cycle signal,

wherein said calibration method reduces the incidence of failedcalibration for the analyte monitoring system.

In the above-described method for reducing the incidence of failedcalibration, before performing said calibration method, a ratio of thesignals obtained from the first sensor (A) and the second sensor (B) maybe determined based on a series of signals obtained from first sensor(A) and second sensor (B), said ratio representing the relationshipbetween sensor signals. One or more microprocessors may be programmed toprovide the ratio.

The ratio of signals can be a smoothed ratio of the form:R _(i) ^(s) =wR _(i)+(1−w)R _(i−1) ^(s)  Eqn. 10

wherein, R_(i) is the A/B or B/A ratio for a i^(th) measurement cycle,R^(S) _(i) is smoothed R for a i^(th) measurement cycle, and w is asmoothing factor and represents a numerical, percentage value betweenand inclusive of 0 through 100%, where w is represented by a fractionbetween and inclusive of 0 through 1, and R^(S) _(i−1) is a smoothedratio for the (i−1)^(th) measurement cycle, wherein the i^(th)measurement cycle is composed of first and second half-cycles and thesecond half-cycle value of the i^(th) measurement cycle precedes S_(j).A single R^(S) _(i) may be used or more than one such ratio may beemployed.

In one embodiment of the method for reducing the incidence of failedcalibration in a two sensor system, two smoothed AB ratios may beemployed: $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\mspace{11mu}\text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\mspace{14mu}\text{9B}}\end{matrix}$

In Eqn. 9A and Eqn. 9B, (A/B)_(s,i) and (B/A)_(s,i) refer to “smoothed”AB ratios for measurement cycle i, (A/B)_(i) and (B/A)_(i), refer to theAB ratio for measurement cycle i, and (A/B)_(s,i−1) and (B/A)_(s,i−1),refer to the smoothed AB ratio from the previous measurement cycle i−1.In the Holt-Winters smoothing presented above, the determination of thesmoothed AB ratio depends on the adjustable parameter w (a weightingfactor). In one embodiment of the present invention, w is 70% (0.70).

In one embodiment of the method for reducing the incidence of failedcalibration in a two-sensor system (wherein two AB ratios are employed,conditional statement (b) is selected, and said interpolated value isdetermined by an interpolation calculation) Eqn. 7A through Eqn. 7D maybe employed for interpolation in the following situations:

in the situation where both S_(j) and S_(j+2) are signals from the Bsensor (S^(B) _(j) and S^(B) _(j+2)), and S_(j+1) is being estimated forthe A sensor signal (S^(AE) _(j+1)), interpolation Eqn. 7A may beemployed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\mspace{14mu}\text{7A}}\end{matrix}$

wherein t is the time interval, for example, measurement half-cyclet_(j), one subsequent half-cycle, t_(j+1), or two subsequent half-cyclest_(j+2). When the points are equally spaced, that is when2(t_(j+1)−t_(j))=(t_(j+2)−t_(j)), then Eqn. 7A reduces to the followingEqn. 7B: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left( \frac{S_{j}^{B} + S_{j + 2}^{B}}{2} \right)}} & {{Eqn}.\mspace{11mu}\text{7B}}\end{matrix}$

In the situation where both S_(j) and S_(j+2) are signals from the Asensor (S^(A) _(j) and S^(A) _(j+2)), and S_(j+1) is being estimated forthe B sensor signal (S^(BE) _(j+1)), interpolation Eqn. 7C may beemployed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\mspace{14mu}\text{7C}}\end{matrix}$

When the points are equally spaced, that is when2(t_(j+1)−t_(j))=(t_(j+2)−t_(j)), then Eqn. 7C reduces to the followingEqn. 7D: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}{\left( \frac{S_{j}^{A} + S_{j + 2}^{A}}{2} \right).}}} & {{Eqn}.\mspace{14mu}\text{7D}}\end{matrix}$

In a further embodiment of the method for reducing the incidence offailed calibration in a two sensor system (wherein two AB ratios areemployed, conditional statement (c) is selected, and said extrapolatedvalue is determined by an extrapolation method) Equations 2A and 2B maybe employed for extrapolation in the following situations:

in the situation where S_(j) is signal from sensor A (S^(A) _(j)) andS_(j+1) is signal from B sensor (S^(B) _(j+1)), and S_(j+2) is beingestimated for the A sensor signal (S^(AE) _(j+2)), extrapolation Eqn. 8Amay be employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\}\frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\mspace{14mu}\text{8A}}\end{matrix}$

When the points are equally spaced, that is when(t_(j+2)−t_(j+1))=(t_(j+1)−t_(j)), then Eqn. 8A reduces to the followingEqn. 8B: $\begin{matrix}{S_{j + 2}^{AE} = {{2\frac{A}{B}S_{j + 1}^{B}} - S_{j}^{A}}} & {{Eqn}.\mspace{14mu}\text{8B}}\end{matrix}$

In the situation where S_(j) is signal from the B sensor (S^(B) _(j))and S_(j+1) is signal from the A sensor (S^(A) _(j+1)), and S_(j+2) isbeing estimated for the B sensor signal (S^(BE) _(j+2)), extrapolationEqn. 8C may be employed as follows: $\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\}\frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\mspace{14mu}\text{8C}}\end{matrix}$

When the points are equally spaced, that is when(t_(j+2)−t_(j+1))=(t_(j+1)−t_(j)), then Eqn. 8C reduces to the followingEqn. 8D: $\begin{matrix}{S_{j + 2}^{BE} = {{2\frac{B}{A}S_{j + 1}^{A}} - {S_{j}^{B}.}}} & {{Eqn}.\mspace{14mu}\text{8D}}\end{matrix}$

In these embodiments of the present invention one or moremicroprocessors may be utilized to execute the interpolation methods,extrapolation methods, and/or the method for reducing the incidence offailed calibration, as well as to control components of an analytemonitoring system, for example, control extracting samples, sensinganalyte concentration in each obtained sample, and selecting conditionalstatements based on obtained criteria.

In this third aspect of the invention, the method may further comprisewaiting for an un-skipped half-cycle signal (S_(j)) before initiatingthe calibration method.

In one embodiment of this third aspect of the present invention, theanalyte is glucose. The analyte monitoring device may, for example,comprise (i) an iontophoretic sampling device, and (ii) anelectrochemical sensing device. The analyte-related signal may be, e.g.,a current or a charge related to analyte amount or concentration ofanalyte in the subject.

One or more microprocessors may be utilized to control operation of thecalibration method employing the methods described herein. Further, suchone or more microprocessors may be used to control operation of thecomponents of the analyte monitoring system (e.g., a sampling device anda sensing device of the monitoring system). In addition, the one or moremicroprocessors may control operation of other components, furtheralgorithms, calculations, and/or the providing of alerts to a subject(user of the analyte monitoring system).

The present invention also includes analyte monitoring devices employingthe above methods.

In a fourth aspect, the present invention teaches a method comprisingwaiting for an unskipped (i.e., error free or good signal) half-cyclesignal before initiating a calibration sequence (e.g., before opening acalibration window inviting the user to provide to a monitoring systeman independently determined analyte calibration value).

In a fifth aspect, the present invention describes methods forpredicting an analyte concentration-related event when an analyte levelfalls above or below predetermined thresholds or outside of apredetermined range of reference values, microprocessors programmed toexecute these methods, and analyte monitoring systems employing thesemethods. The methods provide for predicting an analyteconcentration-related event in a subject being monitored for levels of aselected analyte. The methods of the invention typically employ multipleparameters to be used in prediction of the hypoglycemic event. Suchparameters include, but are not limited to, current glucose readings(reflecting glucose amount or concentration in the subject), one or morepredicted future glucose reading, time intervals, trends, skinconductance, and skin temperature. In one aspect, the analyte beingmonitored is glucose and the present invention comprises a method forpredicting a hypoglycemic and/or hyperglycemic event in a subject.

The method comprises determining threshold values (or ranges of values)for the selected parameters, wherein the threshold values (or ranges ofvalues) are indicative of an analyte concentration-related event in thesubject: e.g., determining a threshold glucose value (or range ofvalues) that corresponds to a hypoglycemic event. A series of analytemeasurement values is typically obtained at selected time intervals. Inone embodiment the time intervals are evenly spaced. Such a series maybe obtained, for example, using a method comprising: extracting a samplecomprising the analyte, e.g., glucose, from the subject using atransdermal sampling system that is in operative contact with a skin ormucosal surface of the subject; obtaining a raw signal from theextracted analyte, wherein the raw signal is specifically related toanalyte amount or concentration in the subject; correlating the rawsignal with an analyte measurement value indicative of the amount orconcentration of analyte present in the subject at the time ofextraction; and repeating the extracting, obtaining, and correlating toprovide a series of measurement values at selected time intervals. Inone embodiment, the sampling system used to extract samples ismaintained in operative contact with the skin or mucosal surface of thesubject during the extracting, obtaining, and correlating to provide forfrequent analyte measurements (e.g., glucose measurements).

In this aspect of the present invention, one or more gradient methodsmay be employed to examine the trend of analyte values, and/or one ormore predictive algorithms may be employed to predict an analytemeasurement value for a further time interval subsequent to the seriesof measurement values. In one embodiment of this aspect of the presentinvention, the series of measurement values comprises two or morediscrete values.

Several models for the determination of a gradient (i.e., the rate ofchange) are as follows: $\begin{matrix}{{{Model}\mspace{14mu} A\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 1})}}{\Delta\; t}\mspace{11mu}\left( {{concentration}/{time}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}} \\{{{Model}\mspace{14mu} B\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 1})}}{y_{({n - 1})}\Delta\; t}\mspace{11mu}\left( {{fractional}\mspace{14mu}{{change}/{time}}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}} \\{{{Model}\mspace{14mu} C\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 2})}}{\Delta\; t}\mspace{11mu}\left( {{concentration}/{time}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\mspace{14mu} D\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 2})}}{y_{({n - 2})}\Delta\; t}\mspace{11mu}\left( {{fractional}\mspace{14mu}{{change}/{time}}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\mspace{14mu} E\text{:}\mspace{14mu}{{Average}\;\left\lbrack {\frac{y_{(n)} - y_{({n - 1})}}{\Delta\; t_{1}},\frac{y_{({n - 1})} - y_{({n - 2})}}{\Delta\; t_{2}},\frac{y_{(n)} - y_{({n - 2})}}{\Delta\; t_{3}}} \right\rbrack}}\mspace{11mu}} \\{\mspace{115mu}{\left( {{concentration}/{time}} \right);}}\end{matrix}$where

Δt₁=(t_((n))−t_((n−1))), Δt₂=(t_((n−1))−t_((n−2))), andΔt₃=(t_((n))−t_((n−2))). In this model, the average is of all threevalues shown in the brackets.${{Model}\mspace{14mu} F\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 3})}}{y_{({n - 3})}\Delta\; t}\mspace{11mu}\left( {{fractional}\mspace{14mu}{{change}/{time}}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 3})}} \right)}$

In the above models, y_(n) stands for an analyte reading at time pointt_((n)), y_((n−1)) an analyte reading at time point t_((n−1)) (i.e., theprevious reading to y_(n)), y_((n−2)) an analyte reading at time pointt_((n−2)) (i.e., the reading previous to y_((n−1))), y_((n−3)) ananalyte reading at time point t_((n−3)) (i.e., the reading previous toy_((n−2))). Each of the above methods give a rate of change. Models A,C, and E give concentration change per time interval (e.g., for glucosemg/dL/minute (milligrams of glucose per deciliter per minute) ormmol/L/minute), whereas Models B, D, and F gives fractional change pertime interval (e.g., a percentage change in the glucose reading perminute). When using a gradient method a threshold of an acceptable rateof change is selected (for example, based on experimental data and/oracceptable ranges of measurement values).

The selected model calculates the rate of change (e.g., in the indicatedunits) and an algorithm compares the calculated rate of change to theacceptable rate of change. If the calculated rate of change surpassesthe acceptable rate of change then a alert may be provided to the user.In one embodiment, a microprocessor employs an algorithm comprising theselected model and calculates the rate of change (e.g., in the indicatedunits). The microprocessor then employs an algorithm to compare thecalculated rate of change to a predetermined acceptable rate of change.If the calculated rate of change differs significantly from theacceptable rate of change then the microprocessor triggers the analytemonitoring system to provide an alert to the user. Typically whenemploying the gradient models, to provide a low-analyte alert (e.g.,hypoglycemic event alert) the calculated rate of change is negative andless than the predetermined threshold rate of change; and/or to providea high-analyte alert (e.g., hyperglycemic event alert) the calculatedrate of change is positive and greater than the predetermined thresholdrate of change. Alternatively, absolute values of the calculated andthreshold rates of change may be used for comparison. In this case, analert is provided when the absolute value of the calculated rate ofchange is greater than the absolute value of predetermined thresholdrate of change.

Exemplary predictive algorithm methods include, but are not limited to,the following: $\begin{matrix}{y_{({n + 1})} = {y_{(n)} + {\alpha\left( {y_{(n)} - y_{({n - 1})}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{(n)} - {2y_{({n - 1})}} + y_{({n - 2})}} \right)}}} & {{Eqn}.\mspace{14mu} 11} \\{y_{({n + 1})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 1})} - t_{n}} \right)}}} & {{Eqn}.\mspace{14mu} 12} \\{y_{({n + 1})} = {{\frac{5}{2}y_{(n)}} + {{- 2}\left( y_{({n - 1})} \right)} + {\frac{1}{2}\left( y_{({n - 2})} \right)}}} & {{Eqn}.\mspace{14mu} 13} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 2})}} \right)}{\left( {t_{n} - t_{({n - 2})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\mspace{14mu} 14} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\mspace{14mu} 15}\end{matrix}$

In these equations, the methods calculate the predicted value of avariable y at time t_(n+1) (or t_(n+2), as indicated) as a function ofthat variable at the current time t_(n), as well as at a previous timeor times, e.g., t_(n−1) and/or t_(n−2)). In Eqn. 11, α is an empiricallydetermined weighting value that is typically a real number between 0and 1. Each of the above methods provides a predicted analyte value, forexample, an amount or concentration (e.g., the units may be mg/dL ormmol/L when y is a glucose reading). When using a predictive algorithm,thresholds of an acceptable range for analyte amount or concentrationare selected (for example, based on experimental data and/or acceptableranges of measurement values). High threshold values may be selected(e.g., a glucose value that is considered hyperglycemic for a subject),low threshold values may be selected (e.g., a glucose value that isconsidered hypoglycemic for a subject), and/or an acceptable range ofvalues with an associated error may also be employed.

In one embodiment, one or more microprocessors employ an algorithmcomprising the selected predictive algorithm and calculates thepredicted value (e.g., in the indicated units). The microprocessor thenemploys an algorithm to compare the predicted value to the thresholdvalue(s). If the predicted value falls above a high threshold, below alow threshold, or outside of a predetermined range of values, then themicroprocessor triggers the analyte monitoring system to provide analert to the user.

When the analyte being monitored is glucose and glucose readings areprovided by a glucose monitoring device y_(n) corresponds to GW_(n), aglucose value in the subject at time t_(n). Further, for prediction ofglucose values when using Eqn. 11, α is typically in the range of0.5–0.7.

In a further embodiment of this aspect of the present invention, anapproach combining the above-described gradient method and predictivealgorithm method is employed. In this embodiment of the presentinvention, rate of change thresholds are determined as well as analytethresholds (or range of values). Generally, a predictive algorithm ischosen which provides a predicted analyte value at a future time point.The predicted value is compared to the threshold value for the alert. Ifthe predicted value exceeds the threshold value, then the rate of changeof the analyte is evaluated. If the rate of change of the analyte levelalso surpasses a predetermined threshold (or falls outside of a range ofvalues) then an analyte concentration-related alert is provided to thesubject in whom the analyte levels are being monitored. Of course theorder of these two comparisons (i.e., predicted value and rate ofchange) may be reversed, for example, where the rate of change isevaluated first and then the predicted value is evaluated. In oneembodiment of the present invention, a future analyte measurement value(e.g., a glucose measurement value) is predicted using Eqn. 11 and agradient analysis is performed using Model B.

When employing the above gradient methods and/or predictive algorithms,an alert/alarm can be used to notify the subject (or user) if thepredicted value is above/below a predetermined threshold.

In a further embodiment of the present invention, the rolling valuesdescribed above are employed as the measurement data points in theanalyte concentration-related alert methods. In yet a further aspect ofthe present invention interpolation and/or extrapolation methods areemployed to provide missing or error-associated signals in the series ofanalyte-related signals.

One or more microprocessors may be utilized to control prediction of ananalyte-concentration related event employing the methods describedherein. Further, such one or more microprocessors may be used to controloperation of the components of the analyte monitoring system (e.g., asampling device and a sensing device of the monitoring system). Inaddition, the one or more microprocessors may control operation of othercomponents, further algorithms, calculations, and/or the providing ofalerts to a subject (user of the analyte monitoring system).

The present invention also includes analyte monitoring devices employingthe above methods.

In one embodiment of this aspect of the present invention, the samplecomprising glucose is extracted from the subject into a collectionreservoir to obtain an amount or concentration of glucose in thereservoir. Such one or more collection reservoirs are typically incontact with the skin or mucosal surface of the subject and the sampleis extracted using an iontophoretic current applied to the skin ormucosal surface. Further, at least one collection reservoir may comprisean enzyme that reacts with the extracted glucose to produce anelectrochemically detectable signal, e.g., glucose oxidase.Alternatively, the series of glucose measurement values may be obtainedwith a different device, for example, using a near-infraredspectrometer.

This aspect of the present invention also comprises a glucose monitoringsystem useful for performing the methods of the present invention. Inone embodiment, the glucose monitoring system comprises, in operativecombination, a sensing mechanism (in operative contact with the subjector with a glucose-containing sample extracted from the subject, whereinthe sensing mechanism obtains a raw signal specifically related toglucose amount or concentration in the subject), and one or moremicroprocessors in operative communication with the sensing mechanism.The microprocessors comprise programming to (i) control the sensingmechanism to obtain a series of raw signals at selected time intervals,(ii) correlate the raw signals with measurement values indicative of theamount or concentration of glucose present in the subject to obtain aseries of measurement values, (iii) predict a measurement value at afurther time interval, which occurs after the series of measurementvalues is obtained, (iv) compare the predicted measurement value to apredetermined threshold value or range of values, wherein a predictedmeasurement value lower than the predetermined threshold value isdesignated to be hypoglycemic, (v) calculate a gradient, (vi) comparethe gradient value to a predetermined threshold value/trend or range ofvalues/trends, wherein when the calculated rate of change is negativeand less than the predetermined threshold rate of change this isindicative of a hypoglycemic event; and (vii) predict a hypoglycemicevent in the subject when both (a) comparing the predicted measurementvalue to the threshold glucose value (or range of values) indicates ahypoglycemic event, and (b) comparing the gradient reading with athreshold parameter value, range of values, or trend of parameter valuesindicates a hypoglycemic event.

Embodiments of all of the above aspects of the present invention mayinclude application of sampling techniques/devices including, but notlimited to, the following: iontophoresis, sonophoresis, suction,electroporation, thermal poration, laser poration, passive diffusion,microfine (miniature) lances or cannulas, biolistic, subcutaneousimplants or insertions, implanted sensing devices (e.g., in a bodycavity, blood vessel, or under a surface tissue layer) as well as laserdevices. In a preferred embodiment the method of extraction comprisesuse of a sampling device that provides transdermal extraction. In apreferred embodiment, the method of sensing the analyte, comprises useof a sensing device to obtain analyte-related signals. Examples of suchsensing devices include, but are not limited to, a biosensor or aninfrared sensor. In one embodiment of the present invention, the analytecomprises glucose. In one embodiment of the above methods, the analytecomprises glucose and said analyte monitoring system comprises atransdermal sampling device and a biosensor. In one embodiment of theabove methods, one or more microprocessors are programmed to execute themethods. Additionally, said one or more microprocessors may beprogrammed to control said sampling and sensing devices. Further, theinvention also includes an analyte monitoring system that employs anyone or more of the above-described methods, said monitoring systemcomprising one or more microprocessors programmed to control a samplingdevice, a sensing device, data acquisition, and data manipulationassociated with the methods.

These and other embodiments of the present invention will readily occurto those of ordinary skill in the art in view of the disclosure herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 presents a schematic diagram showing A and B biosensor signals,“B/A” averages and additional “moving average” (“A/B”) measurementcycles for 6-readings-per-hour processing versus 3-readings-per-hourprocessing.

FIG. 2 presents a schematic of an exploded view of exemplary componentscomprising one embodiment of an autosensor for use in a monitoringsystem.

FIGS. 3A, 3B, and 3C illustrate three different read frequencies schemesranging from serial paired measurements (AB, AB, AB; FIG. 3A), to a“rolling value” measurement (AB, BA, AB, BA; FIG. 3B), to an “integralsplit” measurement, where readings are provided most frequently (FIG.3C).

FIG. 4A illustrates how the newest trapezoidal segment replaces theoldest one as a new current value is taken in the integral splittingmethod. FIG. 4B illustrates the increase in reading frequency forvarious measurement methods (e.g., integral splitting and rolling valuesrelative to serial paired).

FIG. 5 illustrates a situation wherein analyte readings are missed by ananalyte monitoring system following a failed recalibration attempt,until a successful recalibration is performed.

FIG. 6 illustrates a situation wherein analyte readings are not missedby an analyte monitoring system following a failed recalibration attemptbecause the system reverts to using a previous calibration until asuccessful recalibration is performed.

DETAILED DESCRIPTION OF THE INVENTION

All publications, patents and patent applications cited herein arehereby incorporated by reference in their entireties.

1. Definitions

It is to be understood that the terminology used herein is for thepurpose of describing particular embodiments only, and is not intendedto be limiting. As used in this specification and the appended claims,the singular forms “a”, “an” and “the” include plural referents unlessthe context clearly dictates otherwise. Thus, for example, reference to“a reservoir” includes a combination of two or more such reservoirs,reference to “an analyte” includes mixtures of analytes, and the like.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the invention pertains. Although other methods andmaterials similar, or equivalent, to those described herein can be usedin the practice of the present invention, the preferred materials andmethods are described herein.

In describing and claiming the present invention, the followingterminology will be used in accordance with the definitions set outbelow.

The term “microprocessor” refers to a computer processor contained on anintegrated circuit chip, such a processor may also include memory andassociated circuits. A microprocessor may further comprise programmedinstructions to execute or control selected functions, computationalmethods, switching, etc. Microprocessors and associated devices arecommercially available from a number of sources, including, but notlimited to, Cypress Semiconductor Corporation, San Jose, Calif.; IBMCorporation, White Plains, N.Y.; Applied Microsystems Corporation,Redmond, Wash.; Intel Corporation, Chandler, Ariz.; and, NationalSemiconductor, Santa Clara, Calif.

The terms “analyte” and “target analyte” are used to denote anyphysiological analyte of interest that is a specific substance orcomponent that is being detected and/or measured in a chemical,physical, enzymatic, or optical analysis. A detectable signal (e.g., achemical signal or electrochemical signal) can be obtained, eitherdirectly or indirectly, from such an analyte or derivatives thereof.Furthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

A “sampling device,” “sampling mechanism” or “sampling system” refers toany device and/or associated method for obtaining a sample from abiological system for the purpose of determining the concentration of ananalyte of interest. Such “biological systems” include any biologicalsystem from which the analyte of interest can be extracted, including,but not limited to, blood, interstitial fluid, perspiration and tears.Further, a “biological system” includes both living and artificiallymaintained systems. The term “sampling” mechanism refers to extractionof a substance from the biological system, generally across a membranesuch as the stratum corneum or mucosal membranes, wherein said samplingis invasive, minimally invasive, semi-invasive or non-invasive. Themembrane can be natural or artificial, and can be of plant or animalnature, such as natural or artificial skin, blood vessel tissue,intestinal tissue, and the like. Typically, the sampling mechanism is inoperative contact with a “reservoir,” or “collection reservoir,” whereinthe sampling mechanism is used for extracting the analyte from thebiological system into the reservoir to obtain the analyte in thereservoir. Non-limiting examples of sampling techniques includeiontophoresis, sonophoresis (see, e.g., International Publication No. WO91/12772, published 5, Sep. 1991; U.S. Pat. No. 5,636,632), suction,electroporation, thermal poration, passive diffusion (see, e.g.,International Publication Nos.: WO 97/38126 (published 16, Oct. 1997);WO 97/42888, WO 97/42886, WO 97/42885, and WO 97/42882 (all published20, Nov. 1997); and WO 97/43962 (published 27, Nov. 1997)), microfine(miniature) lances or cannulas, biolistic (e.g., using particlesaccelerated to high speeds), subcutaneous implants or insertions, andlaser devices (see, e.g., Jacques et al. (1978) J. Invest. Dermatology88:88–93; International Publication WO 99/44507, published 1999 Sep. 10;International Publication WO 99/44638, published 1999 Sep. 10; andInternational Publication WO 99/40848, published Aug. 19, 1999).Iontophoretic sampling devices are described, for example, inInternational Publication No. WO 97/24059, published 10 Jul. 1997;European Patent Application EP 0942 278, published 15 Sep. 1999;International Publication No. WO 96/00110, published 4 Jan. 1996;International Publication No. WO 97/10499, published 2 Mar. 1997; U.S.Pat. Nos. 5,279,543; 5,362,307; 5,730,714; 5,771,890; 5,989,409;5,735,273; 5,827,183; 5,954,685 and 6,023,629, 6,298,254, all of whichare herein incorporated by reference in their entireties. Further, apolymeric membrane may be used at, for example, the electrode surface toblock or inhibit access of interfering species to the reactive surfaceof the electrode.

The term “physiological fluid” refers to any desired fluid to besampled, and includes, but is not limited to, blood, cerebrospinalfluid, interstitial fluid, semen, sweat, saliva, urine and the like.

The term “artificial membrane” or “artificial surface,” refers to, forexample, a polymeric membrane, or an aggregation of cells of monolayerthickness or greater which are grown or cultured in vivo or in vitro,wherein said membrane or surface functions as a tissue of an organismbut is not actually derived, or excised, from a pre-existing source orhost.

A “monitoring system” refers to a system useful for obtaining frequentmeasurements of a physiological analyte present in a biological system.Such a system may comprise, but is not limited to, a sampling mechanism,a sensing mechanism, and a microprocessor mechanism in operativecommunication with the sampling mechanism and the sensing mechanism.

A “measurement cycle” typically comprises extraction of an analyte froma subject, using, for example, a sampling device, and sensing of theextracted analyte, for example, using a sensing device, to provide ameasured signal, for example, a measured signal response curve. Acomplete measurement cycle may comprise one or more sets of extractionand sensing.

The term “frequent measurement” refers to a series of two or moremeasurements obtained from a particular biological system, whichmeasurements are obtained using a single device maintained in operativecontact with the biological system over a time period in which a seriesof measurements (e.g, second, minute or hour intervals) is obtained. Theterm thus includes continual and continuous measurements.

The term “subject” encompasses any warm-blooded animal, particularlyincluding a member of the class Mammalia such as, without limitation,humans and nonhuman primates such as chimpanzees and other apes andmonkey species; farm animals such as cattle, sheep, pigs, goats andhorses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex and, thus, includes adultand newborn subjects, whether male or female.

The term “transdermal” includes both transdermal and transmucosaltechniques, i.e., extraction of a target analyte across skin, e.g.,stratum corneum, or mucosal tissue. Aspects of the invention which aredescribed herein in the context of “transdermal,” unless otherwisespecified, are meant to apply to both transdermal and transmucosaltechniques.

The term “transdermal extraction,” or “transdermally extracted” refersto any sampling method, which entails extracting and/or transporting ananalyte from beneath a tissue surface across skin or mucosal tissue. Theterm thus includes extraction of an analyte using, for example,iontophoresis (reverse iontophoresis), electroosmosis, sonophoresis,microdialysis, suction, and passive diffusion. These methods can, ofcourse, be coupled with application of skin penetration enhancers orskin permeability enhancing technique such as various substances orphysical methods such as tape stripping or pricking with micro-needles.The term “transdermally extracted” also encompasses extractiontechniques which employ thermal poration, laser microporation,electroporation, microfine lances, microfine cannulas, subcutaneousimplants or insertions, combinations thereof, and the like.

The term “iontophoresis” refers to a method for transporting substancesacross tissue by way of an application of electrical energy to thetissue. In conventional iontophoresis, a reservoir is provided at thetissue surface to serve as a container of (or to provide containmentfor) material to be transported. lontophoresis can be carried out usingstandard methods known to those of skill in the art, for example byestablishing an electrical potential using a direct current (DC) betweenfixed anode and cathode “iontophoretic electrodes,” alternating a directcurrent between anode and cathode iontophoretic electrodes, or using amore complex waveform such as applying a current with alternatingpolarity (AP) between iontophoretic electrodes (so that each electrodeis alternately an anode or a cathode). For example, see U.S. Pat. Nos.5,771,890 and 6,023,629 and PCT Publication No. WO 96/00109, published 4Jan. 1996.

The term “reverse iontophoresis” refers to the movement of a substancefrom a biological fluid across a membrane by way of an applied electricpotential or current. In reverse iontophoresis, a reservoir is providedat the tissue surface to receive the extracted material, as used in theGlucoWatch® (Cygnus, Inc., Redwood City, Calif.) biographer glucosemonitor (See, e.g., Tamada et al. (1999) JAMA 282:1839–1844).

“Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

The term “sensing device,” or “sensing mechanism,” encompasses anydevice that can be used to measure the concentration or amount of ananalyte, or derivative thereof, of interest. Preferred sensing devicesfor detecting blood analytes generally include electrochemical sensordevices, optical sensor devices, chemical sensor devices, andcombinations thereof. Examples of electrochemical devices include theClark electrode system (see, e.g., Updike, et al., (1967) Nature214:986–988), and other amperometric, coulometric, or potentiometricelectrochemical devices, as well as, optical methods, for example UVdetection or infrared detection (e.g., U.S. Pat. No. 5,747,806). Othersensing devices include, but are not limited to, implanted sensingdevices (e.g., in a body cavity, blood vessel, under skin, or under asurface tissue layer).

A “biosensor” or “biosensor device” includes, but is not limited to, a“sensor element” that includes, but is not limited to, a “biosensorelectrode” or “sensing electrode” or “working electrode” which refers tothe electrode that is monitored to determine the amount of electricalsignal at a point in time or over a given time period, which signal isthen correlated with the concentration of a chemical compound. Thesensing electrode comprises a reactive surface which converts theanalyte, or a derivative thereof, to electrical signal. The reactivesurface can be comprised of any electrically conductive material suchas, but not limited to, platinum-group metals (including, platinum,palladium, rhodium, ruthenium, osmium, and iridium), nickel, copper, andsilver, as well as, oxides, and dioxides, thereof, and combinations oralloys of the foregoing, which may include carbon as well. Somecatalytic materials, membranes, and fabrication technologies suitablefor the construction of amperometric biosensors are described by Newman,J. D., et al. (1995) Analytical Chemistry 67:4594–4599. A sensing devicemay, for example, comprises one or more sensing electrodes. Alternately,a sensing device may, for example, comprise two or more sensingelectrodes. In a further embodiment, a sensing device may, for example,comprise an array of sensing electrodes comprising greater than twoelectrodes.

The “sensor element” can include components in addition to the sensingelectrode, for example, it can include a “reference electrode” and a“counter electrode.” The term “reference electrode” is used to mean anelectrode that provides a reference potential, e.g., a potential can beestablished between a reference electrode and a working electrode. Theterm “counter electrode” is used to mean an electrode in anelectrochemical circuit that acts as a current source or sink tocomplete the electrochemical circuit. Although it is not essential thata counter electrode be employed where a reference electrode is includedin the circuit and the electrode is capable of performing the functionof a counter electrode, it is preferred to have separate counter andreference electrodes because the reference potential provided by thereference electrode is most stable when it is at equilibrium. If thereference electrode is required to act further as a counter electrode,the current flowing through the reference electrode may disturb thisequilibrium. Consequently, separate electrodes functioning as counterand reference electrodes are preferred.

In one embodiment, the “counter electrode” of the “sensor element”comprises a “bimodal electrode.” The term “bimodal electrode” typicallyrefers to an electrode which is capable of functioningnon-simultaneously as, for example, both the counter electrode (of the“sensor element”) and the iontophoretic electrode (of the “samplingmechanism”) as described, for example, U.S. Pat. No. 5,954,685.

The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an ionically conductivematerial which contains an analyte or through which an analyte, or aderivative thereof, flows from a source thereof; (2) is comprised of acatalytic material (e.g., a platinum group metal, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (for example, hydrogenperoxide) into an electrical signal (e.g., an electrical current); and(4) defines the electrode surface area that, when composed of a reactivematerial, is sufficient to drive the electrochemical reaction at a ratesufficient to generate a detectable, reproducibly measurable, electricalsignal when an appropriate electrical bias is supplied, that iscorrelatable with the amount of analyte present in the electrolyte.

An “ionically conductive material” refers to any material that providesionic conductivity, and through which electrochemically active speciescan diffuse. The ionically conductive material can be, for example, asolid, liquid, or semi-solid (e.g., in the form of a gel) material thatcontains an electrolyte, which can be composed primarily of water andions (e.g., sodium chloride), and generally comprises 50% or more waterby weight. The material can be in the form of a hydrogel, a sponge orpad (e.g., soaked with an electrolytic solution), or any other materialthat can contain an electrolyte and allow passage of electrochemicallyactive species, especially the analyte of interest. Some exemplaryhydrogel formulations are described in WO 97/02811, published Jan. 30,1997, and WO 0064533A1, published Nov. 2, 2000, both herein incorporatedby reference. The ionically conductive material may comprise a biocide.For example, during manufacture of an autosensor assembly, one or morebiocides may be incorporated into the ionically conductive material.Biocides of interest include, but are not limited to, compounds such aschlorinated hydrocarbons; organometallics; hydrogen releasing compounds;metallic salts; organic sulfur compounds; phenolic compounds (including,but not limited to, a variety of Nipa Hardwicke Inc. liquidpreservatives registered under the trade names Nipastat®, Nipaguard®,Phenosept®, Phenonip®, Phenoxetol®, and Nipacide®); quaternary ammoniumcompounds; surfactants and other membrane-disrupting agents (including,but not limited to, undecylenic acid and its salts), combinationsthereof, and the like.

The term “buffer” refers to one or more components which are added to acomposition in order to adjust or maintain the pH of the composition.

The term “electrolyte” refers to a component of the ionically conductivemedium which allows an ionic current to flow within the medium. Thiscomponent of the ionically conductive medium can be one or more salts orbuffer components, but is not limited to these materials.

The term “collection reservoir” is used to describe any suitablecontainment method or device for containing a sample extracted from abiological system. For example, the collection reservoir can be areceptacle containing a material which is ionically conductive (e.g.,water with ions therein), or alternatively it can be a material, such asa sponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the shape of a disk or pad). Hydrogels are typicallyreferred to as “collection inserts.” Other suitable collectionreservoirs include, but are not limited to, tubes, vials, strips,capillary collection devices, cannulas, and miniaturized etched, ablatedor molded flow paths.

A “collection insert layer” is a layer of an assembly or laminatecomprising a collection reservoir (or collection insert) located, forexample, between a mask layer and a retaining layer.

A “laminate” refers to structures comprised of, at least, two bondedlayers. The layers may be bonded by welding or through the use ofadhesives. Examples of welding include, but are not limited to, thefollowing: ultrasonic welding, heat bonding, and inductively coupledlocalized heating followed by localized flow. Examples of commonadhesives include, but are not limited to, chemical compounds such as,cyanoacrylate adhesives, and epoxies, as well as adhesives having suchphysical attributes as, but not limited to, the following: pressuresensitive adhesives, thermoset adhesives, contact adhesives, and heatsensitive adhesives.

A “collection assembly” refers to structures comprised of severallayers, where the assembly includes at least one collection insertlayer, for example a hydrogel. An example of a collection assembly asreferred to in the present invention is a mask layer, collection insertlayer, and a retaining layer where the layers are held in appropriatefunctional relationship to each other but are not necessarily a laminate(i.e., the layers may not be bonded together. The layers may, forexample, be held together by interlocking geometry or friction).

The term “mask layer” refers to a component of a collection assemblythat is substantially planar and typically contacts both the biologicalsystem and the collection insert layer. See, for example, U.S. Pat. Nos.5,735,273, 5,827,183, 6,141,573, and 6,201,979, all herein incorporatedby reference.

The term “gel retaining layer” or “gel retainer” refers to a componentof a collection assembly that is substantially planar and typicallycontacts both the collection insert layer and the electrode assembly.

The term “support tray” typically refers to a rigid, substantiallyplanar platform and is used to support and/or align the electrodeassembly and the collection assembly. The support tray provides one wayof placing the electrode assembly and the collection assembly into thesampling system.

An “autosensor assembly” refers to a structure generally comprising amask layer, collection insert layer, a gel retaining layer, an electrodeassembly, and a support tray. The autosensor assembly may also includeliners where the layers are held in approximate, functional relationshipto each other. Exemplary collection assemblies and autosensor structuresare described, for example, in International Publication WO 99/58190,published 18 Nov. 1999; and U.S. Pat. Nos. 5,735,273, 5,827,183,6,141,573, and 6,201,979. The mask and retaining layers are preferablycomposed of materials that are substantially impermeable to the analyte(chemical signal) to be detected; however, the material can be permeableto other substances. By “substantially impermeable” is meant that thematerial reduces or eliminates chemical signal transport (e.g., bydiffusion). The material can allow for a low level of chemical signaltransport, with the proviso that chemical signal passing through thematerial does not cause significant edge effects at the sensingelectrode.

The terms “about” or “approximately” when associated with a numericvalue refers to that numeric value plus or minus 10 units of measure(i.e. percent, grams, degrees or volts), preferably plus or minus 5units of measure, more preferably plus or minus 2 units of measure, mostpreferably plus or minus 1 unit of measure.

By the term “printed” is meant a substantially uniform deposition of anelectrode formulation onto one surface of a substrate (i.e., the basesupport). It will be appreciated by those skilled in the art that avariety of techniques may be used to effect substantially uniformdeposition of a material onto a substrate, e.g., Gravure-type printing,extrusion coating, screen coating, spraying, painting, electroplating,laminating, or the like.

The term “physiological effect” encompasses effects produced in thesubject that achieve the intended purpose of a therapy. In preferredembodiments, a physiological effect means that the symptoms of thesubject being treated are prevented or alleviated. For example, aphysiological effect would be one that results in the prolongation ofsurvival in a patient.

“Parameter” refers to an arbitrary constant or variable so appearing ina mathematical expression that changing it give various cases of thephenomenon represented (McGraw-Hill Dictionary of Scientific andTechnical Terms, S. P. Parker, ed., Fifth Edition, McGraw-Hill Inc.,1994). In the context of the GlucoWatch biographer, a parameter is avariable that influences the value of the blood glucose level ascalculated by an algorithm.

“Decay” refers to a gradual reduction in the magnitude of a quantity,for example, a current detected using a sensor electrode where thecurrent is correlated to the concentration of a particular analyte andwhere the detected current gradually reduces but the concentration ofthe analyte does not.

“Skip” or “skipped” signals refer to data that do not conform topredetermined criteria (for example, error-associated criteria asdescribed in U.S. Pat. No. 6,233,471, herein incorporated by reference).A skipped reading, signal, or measurement value typically has beenrejected (i.e., a “skip error” generated) as not being reliable or validbecause it does not conform with data integrity checks, for example,where a signal is subjected to a data screen which invalidates incorrectsignals based on a detected parameter indicative of a poor or incorrectsignal.

2. General Overview of the Inventions

Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular types ofmicroprocessors, monitoring systems, computational methods or processparameters, as use of such particulars may be selected in view of theteachings of the present specification. It is also to be understood thatthe terminology used herein is for the purpose of describing particularembodiments of the invention only, and is not intended to be limiting.

In one aspect the present invention relates to methods to increase thenumber of analyte-related signals used to provide analyte measurementvalues. Such analyte measurements may, for example, be chemical,physical, enzymatic, or optical. In one embodiment such analytemeasurements are electrochemical, providing, for example, current and/orcharge signals related to analyte amount or concentration. This aspectof the present invention typically applies to the situation where two ormore analyte-related signals are used to obtain a single analytemeasurement value, for example, the sum of two or more values may becorrelated to an analyte amount or concentration, or an average of twoor more values may be correlated to an analyte amount or concentration.

For example, in a two sensor system where analyte-related signals areserially obtained from each sensor in an alternating fashion, ananalyte-related signal from the first sensor (S₁) may be summed with ananalyte-related signal from the second sensor (S₂) to obtain a firstanalyte measurement value (M₁). The measurement cycle is repeated toobtain further analyte-related signals (e.g., S₃, S₄, S₅, S₆, etc.). Inthis example, S₃ and S₄ provide M₂, S₅ and S₆ provide M₃, etc. However,when applying the method of the present invention, the number ofanalyte-related measurement values is doubled. In the method of theinvention each analyte-related signal is paired with its next neighborto obtain a analyte measurement value, for example, S₁ and S₂ provideM₁, S₂ and S₃, provide M₂, S₃ and S₄ provide M₃, S₄ and S₅, provide M₄,etc. Thus the number of analyte-related measurement values is increased(in this example, essentially doubled).

In another aspect of the present invention, each analyte-related signalmay be combined with one (or more) near or next neighbor to obtain,e.g., an average (or summed) analyte measurement value, i.e., theaverage (or summed value) may be obtained using more than twoanalyte-related signals. The number of analyte-related signals that areused to obtain, e.g., an average (or summed) value may be empiricallydetermined by one of ordinary skill in the art following the guidance ofthe present specification. Generally, the averaged (or summed)analyte-related signal should be concordant with the trend of themeasured analyte-related signals.

Another example involves a single sensor system. In this example, theanalyte-related signals are serially obtained from a single sensor, forexample, a first analyte-related signal (S₁), a second analyte-relatedsignal (S₂), S₃, S₄, S₅, etc. The analyte related signals may be pairedto obtain an analyte measurement value, for example, S₁ and S₂ provideM₁, S₃ and S₄provide M₂, etc. In this case, the number ofanalyte-related measurement values may be increased by the method of thepresent invention by pairing each analyte-related signal with its nextneighbor to obtain a analyte measurement value, for example, S₁ and S₂provide M₁, S₂ and S₃, provide M₂, S₃ and S₄ provide M₃, S₄ and S₅,provide M₄, etc.

The present invention also relates to methods of increasing the numberof analyte measurement values related to the amount or concentration ofan analyte in a subject as measured using an analyte monitoring device.In this method a series of analyte-related signals is obtained from theanalyte monitoring device over time. Typically, two or more contiguousanalyte-related signals are used to obtain a single analyte measurementvalue (M). In this method, paired analyte-related signals are typicallyused to calculate the measurement value. One improvement provided by thepresent method is that, prior to the present method, such an analytemonitoring device typically used paired signals to obtain a singlemeasurement value; but an analyte-related signal from the monitoringdevice was not typically used to calculate more than one analytemeasurement value. In the present method, the two or more contiguousanalyte-related signals, used to obtain the single analyte measurementvalue, comprise first and last analyte-related signals of the series.

The method involves mathematically computing rolling analyte measurementvalues, wherein (i) each rolling analyte measurement value is calculatedbased on two or more contiguous analyte-related signals from the seriesof analyte-related signals obtained from the analyte monitoring device.Subsequent rolling analyte measurement values are mathematicallycomputed by dropping the first analyte-related signal from the previousrolling analyte measurement value and including an analyte-relatedsignal contiguous and subsequent to the last analyte-related signal usedto calculate the previous rolling analyte measurement value. Furtherrolling analyte measurement values are obtained by repeating thedropping of the first analyte-related signal used to calculate theprevious rolling analyte measurement and including an analyte-relatedsignal contiguous and subsequent to the last analyte-related signal usedto calculate the previous rolling analyte measurement. Each rollinganalyte measurement value provides a measurement related to the amountor concentration of analyte in the subject. By employing this method thenumber of analyte measurement values, derived from the analyte-relatedsignals in the series of analyte-related signals obtained from theanalyte monitoring device, is increased by serially calculating rollinganalyte measurement values.

In one embodiment of this aspect of the invention, the rolling analytemeasurement value is, for example, an average of two or moreanalyte-related signals; alternately, the rolling analyte measurementvalue is a sum of two or more analyte-related signals. In anotherembodiment, each analyte-related signal is represented by an integralover time, and the rolling analyte measurement value is obtained byintegral splitting.

Missing or error-associated signals in the series of analyte-relatedsignals obtained from the analyte monitoring device may be estimatedusing interpolation before mathematically computing rolling analytemeasurement values. Such missing or error-associated signals may also beestimated using extrapolation before mathematically computing rollinganalyte measurement values.

In a preferred embodiment, the analyte is glucose. In one embodiment,the analyte monitoring device comprises (i) an iontophoretic samplingdevice, and (ii) an electrochemical sensing device. The analyte-relatedsignal may, for example, be a current or a charge related to analyteamount or concentration of analyte in the subject.

Other embodiments of the present invention will be clear to one ofordinary skill in the art in view of the teachings disclosed herein.

In another aspect of the present invention, interpolation and/orextrapolation are used to estimate unusable, missing or error-associatedanalyte-related signals. Such signals may be unusable for a variety ofreasons, typically where an error has been detected that places ananalyte-related signal in question. In the interpolation aspect, aprevious analyte-related signal and a subsequent analyte-related signalare used to estimate the intervening analyte-related signal. Further,one or more previous analyte-related signals, and/or one or moresubsequent analyte-related signals may also be employed forinterpolation. In the extrapolation aspect, two previous analyte-relatedsignals are used to estimate a subsequent analyte-related signal.Further, two or more previous analyte-related signals may also beemployed for extrapolation. Interpolation and extrapolation of values isalso employed in another aspect of the present invention that reducesthe incident of failed calibrations.

One embodiment of this second aspect of the present invention includes amethod of replacing unusable analyte-related signals when employing ananalyte monitoring device to measure an analyte amount or concentrationin a subject. A series of analyte-related signals, obtained from theanalyte monitoring device over time, is provided wherein eachanalyte-related signal is related to the amount or concentration ofanalyte in the subject. An unusable analyte-related signal is replacedwith an estimated signal, for example, by either:

(A) if one or more analyte-related signals previous to the unusableanalyte-related signal and one or more analyte-related signalssubsequent to the unusable analyte related signal are available, theninterpolation is used to estimate the unusable, interveninganalyte-related signal; or

(B) if two or more analyte-related signals previous to the unusableanalyte-related signal are available, then extrapolation is used toestimate the unusable, subsequent analyte-related signal.

In this method, the analyte monitoring device may comprise one or moresensor devices and a relationship between the signals obtained from thedifferent sensor devices is used in interpolation and/or extrapolationcalculation of estimated values. In one embodiment, the sensor devicecomprises two sensor elements and a ratio of signals obtained from afirst sensor relative to a second sensor is employed in interpolationand/or extrapolation calculation of estimated signal values.

In a further aspect of the present invention, methods are described forreducing the incidence of failed calibration for an analyte monitoringsystem that is used to monitor an amount or concentration of analytepresent in a subject.

In another aspect of the present invention, methods are described forproviding an alert related to analyte values exceeding predeterminedthresholds (e.g., high and/or low thresholds) or ranges of values. Inthis aspect of the invention a gradient method and/or predictivealgorithm method may be used.

One or more microprocessors may be utilized to mathematically compute orcontrol execution of algorithms related to any and all of the methodsdescribed herein. Further, such one or more microprocessors may be usedto control operation of the components of the analyte monitoring system(e.g., a sampling device and/or a sensing device of the monitoringsystem). In addition, the one or more microprocessors may controloperation of other components, further algorithms, calculations, and/orthe providing of alerts to a subject (user of the analyte monitoringsystem).

The present invention also includes analyte monitoring devices employingthe above methods.

Although a number of methods and materials similar or equivalent tothose described herein can be used in the practice of the presentinvention, some preferred materials and methods are described herein.

3. Exemplary Monitoring Systems

Numerous analyte monitoring systems can be used in the practice of thepresent invention. Typically, the monitoring system used to monitor thelevel of a selected analyte in a target system comprises a samplingdevice, which provides a sample comprising the analyte, and a sensingdevice, which detects the amount or concentration of the analyte or asignal associated with the analyte amount or concentration in thesample.

One exemplary monitoring system (the GlucoWatch biographer) is describedherein for monitoring glucose levels in a biological system via thetransdermal extraction of glucose from the biological system,particularly an animal subject, and then detection of signalcorresponding to the amount or concentration of the extracted glucose.Transdermal extraction is carried out by applying an electrical currentor ultrasonic radiation to a tissue surface at a collection site. Theelectrical current is used to extract small amounts of glucose from thesubject into a collection reservoir. The collection reservoir is incontact with a sensor element (e.g., a biosensor) which provides formeasurement of glucose concentration in the subject. As glucose istransdermally extracted into the collection reservoir, the analytereacts with the glucose oxidase within the reservoir to produce hydrogenperoxide. The presence of hydrogen peroxide generates a current at thebiosensor electrode that is directly proportional to the amount ofhydrogen peroxide in the reservoir. This current provides a signal whichcan be detected and interpreted (for example, employing a selectedalgorithm) by an associated system controller to provide a glucoseconcentration value or amount for display.

In the use of the sampling system, a collection reservoir is contactedwith a tissue surface, for example, on the stratum corneum of asubject's skin. An electrical current is then applied to the tissuesurface in order to extract glucose from the tissue into the collectionreservoir. Extraction is carried out, for example, frequently over aselected period of time. The collection reservoir is analyzed, at leastperiodically and typically frequently, to measure glucose concentrationtherein. The measured value correlates with the subject's blood glucoselevel.

To sample the analyte, one or more collection reservoirs are placed incontact with a tissue surface on a subject. The ionically conductivematerial within the collection reservoir is also in contact with anelectrode (for reverse iontophoretic extraction) which generates acurrent sufficient to extract glucose from the tissue into thecollection reservoir. Referring to FIG. 2, an exploded view of exemplarycomponents comprising one embodiment of an autosensor for use in aniontophoretic sampling system is presented. The autosensor componentsinclude two biosensor/iontophoretic electrode assemblies, 104 and 106,each of which have an annular iontophoretic electrode, respectivelyindicated at 108 and 110, which encircles a biosensor electrode 112 and114. The electrode assemblies 104 and 106 are printed onto a polymericsubstrate 116 which is maintained within a sensor tray 118. A collectionreservoir assembly 120 is arranged over the electrode assemblies,wherein the collection reservoir assembly comprises two hydrogel inserts122 and 124 retained by a gel retaining layer 126 and mask layer 128.Further release liners may be included in the assembly, for example, apatient liner 130, and a plow-fold liner 132. In an alternativeembodiment, the electrode assemblies can include bimodal electrodes. Amask layer 128 (for example, as described in PCT Publication No. WO97/10356, published 20 Mar. 1997, and U.S. Pat. Nos. 5,735,273,5,827,183, 6,141,573, and 6,201,979, all herein incorporated byreference) may be present. Other autosensor embodiments are described inWO 99/58190, published 18 Nov. 1999, herein incorporated by reference.

The mask and retaining layers are preferably composed of materials thatare substantially impermeable to the analyte (e.g., glucose) to bedetected (see, for example, U.S. Pat. Nos. 5,735,273, and 5,827,183,both herein incorporated by reference). By “substantially impermeable”is meant that the material reduces or eliminates analyte transport(e.g., by diffusion). The material can allow for a low level of analytetransport, with the proviso that the analyte that passes through thematerial does not cause significant edge effects at the sensingelectrode used in conjunction with the mask and retaining layers.Examples of materials that can be used to form the layers include, butare not limited to, polyester, polyester derivatives, otherpolyester-like materials, polyurethane, polyurethane derivatives andother polyurethane-like materials.

The components shown in exploded view in FIG. 2 are intended for use ina automatic sampling system which is configured to be worn like anordinary wristwatch, as described, for example, in PCT Publication No.WO 96/00110, published 4 Jan. 1996, herein incorporated by reference.The wristwatch housing can further include suitable electronics (e.g.,one or more microprocessor(s), memory, display and other circuitcomponents) and power sources for operating the automatic samplingsystem. The one or more microprocessors may control a variety offunctions, including, but not limited to, control of a sampling device,a sensing device, aspects of the measurement cycle (for example, timingof sampling and sensing, and alternating polarity between electrodes),connectivity, computational methods, different aspects of datamanipulation (for example, acquisition, recording, recalling, comparing,and reporting), etc.

The sensing electrode can be, for example, a Pt-comprising electrodeconfigured to provide a geometric surface area of about 0.1 to 3 cm²,preferably about 0.5 to 2 cm², and more preferably about 1 cm². Thisparticular configuration is scaled in proportion to the collection areaof the collection reservoir used in the sampling system of the presentinvention, throughout which the extracted analyte and/or its reactionproducts will be present. The electrode composition is formulated usinganalytical- or electronic-grade reagents and solvents which ensure thatelectrochemical and/or other residual contaminants are avoided in thefinal composition, significantly reducing the background noise inherentin the resultant electrode. In particular, the reagents and solventsused in the formulation of the electrode are selected so as to besubstantially free of electrochemically active contaminants (e.g.,anti-oxidants), and the solvents in particular are selected for highvolatility in order to reduce washing and cure times. Some electrodeembodiments are described in European Patent Publication 0 942 278 A2,published Sep. 15, 1999, herein incorporated by reference.

The reactive surface of the sensing electrode can be comprised of anyelectrically conductive material such as, but not limited to,platinum-group metals (including, platinum, palladium, rhodium,ruthenium, osmium, and iridium), nickel, copper, silver, and carbon, aswell as, oxides, dioxides, combinations or alloys thereof. Somecatalytic materials, membranes, and fabrication technologies suitablefor the construction of amperometric biosensors were described byNewman, J. D., et al. (Analytical Chemistry 67(24), 4594–4599, 1995,herein incorporated by reference).

Any suitable iontophoretic electrode system can be employed, anexemplary system uses a silver/silver chloride (Ag/AgCl) electrodesystem. The iontophoretic electrodes are formulated typically using twoperformance criteria: (1) the electrodes are capable of operation forextended periods, preferably periods of up to 24 hours or longer; and(2) the electrodes are formulated to have high electrochemical purity inorder to operate within the present system which requires extremely lowbackground noise levels. The electrodes must also be capable of passinga large amount of charge over the life of the electrodes. With regard tooperation for extended periods of time, Ag/AgCl electrodes are capableof repeatedly forming a reversible couple which operates withoutunwanted electrochemical side reactions (which could give rise tochanges in pH, and liberation of hydrogen and oxygen due to waterhydrolysis). The Ag/AgCl electrode is thus formulated to withstandrepeated cycles of current passage in the range of about 0.01 to 1.0 mAper cm² of electrode area. With regard to high electrochemical purity,the Ag/AgCl components are dispersed within a suitable polymer binder toprovide an electrode composition which is not susceptible to attack(e.g., plasticization) by components in the collection reservoir, e.g.,the hydrogel composition. The electrode compositions are also typicallyformulated using analytical- or electronic-grade reagents and solvents,and the polymer binder composition is selected to be free ofelectrochemically active contaminants which could diffuse to thebiosensor to produce a background current.

Some exemplary sensors, electrodes, and electrode assemblies aredescribed, for example, in the following United States patents, allherein incorporated by reference in their entireties: U.S. Pat. Nos.5,954,685, 6,139,718, and 6,284,126.

The automatic sampling system can transdermally extract the sample overthe course of a selected period of time using reverse iontophoresis. Thecollection reservoir comprises an ionically conductive medium,preferably the hydrogel medium described hereinabove. A firstiontophoresis electrode is contacted with the collection reservoir(which is typically in contact with a target, subject tissue surface),and a second iontophoresis electrode is contacted with either a secondcollection reservoir in contact with the tissue surface, or some otherionically conductive medium in contact with the tissue. A power sourceprovides an electrical potential between the two electrodes to performreverse iontophoresis in a manner known in the art. As discussed above,the biosensor selected to detect the presence, and possibly the level,of the target analyte (for example, glucose) within a reservoir is alsoin contact with the reservoir. Typically, there are two collectionsreservoirs, each comprising glucose oxidase, and each in operativecontact with iontophoretic electrode and a sensing electrode. Theiontophoretic electrode may be a bimodal electrode that also serves,non-concurrently, as a counter electrode to the sensing electrode (see,for example, U.S. Pat. No. 5,954,685, herein incorporated by reference).

In practice, an electric potential (either direct current or a morecomplex waveform) is applied between the two iontophoresis electrodessuch that current flows from the first electrode through the firstconductive medium into the skin, and back out from the skin through thesecond conductive medium to the second electrode. This current flowextracts substances through the skin into the one or more collectionreservoirs through the process of reverse iontophoresis orelectroosmosis. The electric potential may be applied as described inPCT Publication No. WO 96/00110, published 4 Jan. 1996, hereinincorporated by reference. Typically, the electrical potential isalternated between two reservoirs to provide extraction of analyte intoeach reservoir in an alternating fashion (see, for example, U.S. Pat.Nos. 5,771,890, 6,023,629, 5,954,685, 6,298,254, all herein incorporatedby reference in their entireties). Analyte is also typically detected ineach reservoir.

As an example, to extract glucose, the applied electrical currentdensity on the skin or tissue can be in the range of about 0.01 to about2 mA/cm². In order to facilitate the extraction of glucose, electricalenergy can be applied to the electrodes, and the polarity of theelectrodes can be, for example, alternated so that each electrode isalternately a cathode or an anode. The polarity switching can be manualor automatic. A device and method for sampling of substances usingalternating polarity is described in U.S. Pat. No. 5,771,890, issuedJun. 30, 1998, herein incorporated by reference.

When a bimodal electrode is used (e.g., U.S. Pat. No. 5,954,685, issuedSep. 21, 1999, herein incorporated by reference), during the reverseiontophoretic phase, a power source provides a current flow to the firstbimodal electrode to facilitate the extraction of the chemical signalinto the reservoir. During the sensing phase, a separate power source isused to provide voltage to the first sensing electrode to drive theconversion of chemical signal retained in reservoir to electrical signalat the catalytic face of the sensing electrode. The separate powersource also maintains a fixed potential at the electrode where, forexample hydrogen peroxide is converted to molecular oxygen, hydrogenions, and electrons, which is compared with the potential of thereference electrode during the sensing phase. While one sensingelectrode is operating in the sensing mode it is electrically connectedto the adjacent bimodal electrode which acts as a counter electrode atwhich electrons generated at the sensing electrode are consumed.

The electrode subassembly can be operated by electrically connecting thebimodal electrodes such that each electrode is capable of functioning asboth an iontophoretic electrode and counter electrode along withappropriate sensing electrode(s) and reference electrode(s).

A potentiostat is an electrical circuit used in electrochemicalmeasurements in three electrode electrochemical cells. A potential isapplied between the reference electrode and the sensing electrode. Thecurrent generated at the sensing electrode flows through circuitry tothe counter electrode (i.e., no current flows through the referenceelectrode to alter its equilibrium potential). Two independentpotentiostat circuits can be used to operate the two biosensors. For thepurpose of the present invention, the electrical current measured at thesensing electrode subassembly is the current that is correlated with anamount of chemical signal corresponding to the analyte.

The detected current can be correlated with the subject's blood glucoseconcentration (e.g., using a statistical technique or algorithm orcombination of techniques) so that the system controller may display thesubject's actual blood glucose concentration as measured by the samplingsystem. Such statistical techniques can be formulated as algorithm(s)and incorporated in one or more microprocessor(s) associated with thesampling system. Exemplary signal processing applications include, butare not limited to, those taught in the following U.S. Pat. Nos.6,144,869, 6,233,471, 6,180,416, herein incorporated by reference.Exemplary methods for analyte monitoring include, but are not limitedto, those taught in the following U.S. Pat. Nos. 5,989,409, 6,144,869,6,272,364, 6,299,578, and 6,309,351, all herein incorporated byreference.

In a further aspect of the present invention, the sampling/sensingmechanism and user interface may be found on separate components (see,for example, WO 0047109A1, published Aug. 17, 2000). Thus, themonitoring system can comprise at least two components, in which a firstcomponent comprises sampling mechanism and sensing mechanism that areused to extract and detect an analyte, for example, glucose, and asecond component that receives the analyte data from the firstcomponent, conducts data processing on the analyte data to determine ananalyte concentration and then displays the analyte concentration data.Typically, microprocessor functions (e.g., control of a sampling device,a sensing device, aspects of the measurement cycle, computationalmethods, different aspects of data manipulation or recording, etc.) arefound in both components. Alternatively, microprocessing components maybe located in one or the other of the at least two components. Thesecond component of the monitoring system can assume many forms,including, but not limited to, the following: a watch, a creditcard-shaped device (e.g., a “smart card” or “universal card” having abuilt-in microprocessor as described for example in U.S. Pat. No.5,892,661, herein incorporated by reference), a pager-like device, cellphone-like device, or other such device that communicates information tothe user visually, audibly, or kinesthetically.

Further, additional components may be added to the system, for example,a third component comprising a display of analyte values or an alarmrelated to analyte concentration, may be employed. In certainembodiments, a delivery unit is included in the system. An exemplarydelivery unit is an insulin delivery unit. Insulin delivery units, bothimplantable and external, are known in the art and described, forexample, in U.S. Pat. Nos. 5,995,860; 5,112,614 and 5,062,841, hereinincorporated by reference. Preferably, when included as a component ofthe present invention, the delivery unit is in communication (e.g.,wire-like or wireless communication) with the extracting and/or sensingmechanism such that the sensing mechanism can control the insulin pumpand regulate delivery of a suitable amount of insulin to the subject.

Advantages of separating the first component (e.g., including thebiosensor and iontophoresis functions) from the second component (e.g.,including some microprocessor and display functions) include greaterflexibility, discretion, privacy and convenience to the user. Having asmall and lightweight measurement unit allows placement of the twocomponents of the system on a wider range of body sites, for example,the first component may be placed on the abdomen or upper arm. Thiswider range of placement options may improve the accuracy throughoptimal extraction site selection (e.g., torso rather than extremities)and greater temperature stability (e.g., via the insulating effects ofclothing). Thus, the collection and sensing assembly will be able to beplaced on a greater range of body sites. Similarly, a smaller and lessobtrusive microprocessor and display unit (the second component)provides a convenient and discrete system by which to monitor analytes.The biosensor readouts and control signals will be relayed via wire-likeor wireless technology between the collection and sensing assembly andthe display unit which could take the form of a small watch, a pager, ora credit card-sized device. This system also provides the ability torelay an alert message or signal during nighttime use, for example, to asite remote from the subject being monitored.

In one embodiment, the two components of the device can be in operativecommunication via a wire or cable-like connection. Operativecommunications between the components can be wireless link, i.e.provided by a “virtual cable,” for example, a telemetry link. Thiswireless link can be uni- or bi-directional between the two components.In the case of more than two components, links can be a combination ofwire-like and wireless.

4. Exemplary Analytes

The analyte can be any one or more specific substance, component, orcombinations thereof that one is desirous of detecting and/or measuringin a chemical, physical, enzymatic, or optical analysis.

Analytes that can be measured using the methods of the present inventioninclude, but are not limited to, amino acids, enzyme substrates orproducts indicating a disease state or condition, other markers ofdisease states or conditions, drugs of abuse (e.g., ethanol, cocaine),therapeutic and/or pharmacologic agents (e.g., theophylline, anti-HIVdrugs, lithium, anti-epileptic drugs, cyclosporin, chemotherapeutics),electrolytes, physiological analytes of interest (e.g., urate/uric acid,carbonate, calcium, potassium, sodium, chloride, bicarbonate (CO₂),glucose, urea (blood urea nitrogen), lactate and/or lactic acid,hydroxybutyrate, cholesterol, triglycerides, creatine, creatinine,insulin, hematocrit, and hemoglobin), blood gases (carbon dioxide,oxygen, pH), lipids, heavy metals (e.g., lead, copper), and the like.Analytes in non-biological systems may also be evaluated using themethods of the present invention.

In preferred embodiments, the analyte is a physiological analyte ofinterest, for example glucose, or a chemical that has a physiologicalaction, for example a drug or pharmacological agent.

In order to facilitate detection of the analyte, an enzyme (or enzymes)can be disposed within the one or more collection reservoirs. Theselected enzyme is capable of catalyzing a reaction with the extractedanalyte to the extent that a product of this reaction can be sensed,e.g., can be detected electrochemically from the generation of a currentwhich current is,detectable and proportional to the amount of theanalyte which is reacted. In one embodiment of the present invention, asuitable enzyme is glucose oxidase, which oxidizes glucose to gluconicacid and hydrogen peroxide. The subsequent detection of hydrogenperoxide on an appropriate biosensor electrode generates two electronsper hydrogen peroxide molecule creating a current that can be detectedand related to the amount of glucose entering the device. Glucoseoxidase (GOx) is readily available commercially and has well knowncatalytic characteristics. However, other enzymes can also be usedsingly (for detection of individual analytes) or together (for detectionof multiple analytes), as long as they specifically catalyze a reactionwith an analyte or substance of interest to generate a detectableproduct in proportion to the amount of analyte so reacted.

In like manner, a number of other analyte-specific enzyme systems can beused in the invention, which enzyme systems operate on much the samegeneral techniques. For example, a biosensor electrode that detectshydrogen peroxide can be used to detect ethanol using an alcohol oxidaseenzyme system, or similarly uric acid with urate oxidase system,cholesterol with a cholesterol oxidase system, and theophylline with axanthine oxidase system.

In addition, the oxidase enzyme (used for hydrogen peroxidase-baseddetection) can be replaced or complemented with another redox system,for example, the dehydrogenase-enzyme NAD-NADH, which offers a separateroute to detecting additional analytes. Dehydrogenase-based sensors canuse working electrodes made of gold or carbon (via mediated chemistry).Examples of analytes suitable for this type of monitoring include, butare not limited to, cholesterol, ethanol, hydroxybutyrate,phenylalanine, triglycerides, and urea.

Further, the enzyme can be eliminated and detection can rely on directelectrochemical or potentiometric detection of an analyte. Such analytesinclude, without limitation, heavy metals (e.g., cobalt, iron, lead,nickel, zinc), oxygen, carbonate/carbon dioxide, chloride, fluoride,lithium, pH, potassium, sodium, and urea. Also, the sampling systemdescribed herein can be used for therapeutic drug monitoring, forexample, monitoring anti-epileptic drugs (e.g., phenytoin), chemotherapy(e.g., adriamycin), hyperactivity (e.g., ritalin), andanti-organ-rejection (e.g., cyclosporin).

Preferably, a sensor electrode is able to detect the analyte that hasbeen extracted into the one or more collection reservoirs when presentat nominal concentration levels. Suitable exemplary biosensor electrodesand associated sampling systems as described in are described in PCTPublication Nos. WO 97/10499, published 20 Mar. 1997, WO 98/42252,published 1 Oct. 1998, U.S. Pat. No. 6,284,126, and U.S. Pat. No.6,139,718, all herein incorporated by reference.

A single sensor may detect multiple analytes and/or reaction products ofanalytes. For example, a platinum sensor could be used to detecttyrosine and glucose in a single sample. The tyrosine is detected, forexample, by direct electrochemical oxidation at a suitable electrodepotential (e.g., approximately 0.6V vs. Ag/AgCl ). The glucose isdetected, e.g., using glucose oxidase and detecting the hydrogenperoxide reaction product.

Different sensing devices and/or sensing systems can be employed as wellto distinguish between signals. For example, a first gel containingglucose oxidase associated with a first platinum sensor can be used forthe detection of glucose, while a second gel containing uricaseassociated with a second platinum sensor can be used for the detectionof urea.

5. Methods to Increase the Number of Analyte-Related Signals and ImproveUsability

A. “Rolling Values”

In one aspect, the present invention relates to methods to increase thenumber usable (i.e., good, as in not associated with a significanterror) analyte related signals. In one embodiment the method providesfor obtaining a series of samples comprising the analyte of interest,e.g., glucose, from a subject (e.g., a mammal). This method applies,generally, to monitoring systems that provide a series ofanalyte-related signals over time.

The present invention relates generally to a method for monitoring anamount or concentration of analyte present in a subject, wherein aseries of signals, over time, is provided, and each signal is related tothe analyte amount or concentration in the subject. Multiple signals arethen combined to provide a “rolling value,” for example by:

calculating a series of average signals wherein (i) each average signalis calculated based on two or more contiguous signals (i.e., signalsnext to or near in time or sequence to each other) in the series, and(ii) each average signal provides a measurement related to the amount orconcentration of analyte in the subject; or

calculating a series of sums, wherein (i) each summed signal iscalculated based on two or more contiguous (i.e., next to or near intime or sequence) signals in the series, and (ii) each summed signalprovides a measurement related to the amount or concentration of analytein the subject. Missing signals in the series may be estimated usinginterpolation and/or extrapolation (discussed further below), and suchestimated signals can be used in said calculations.

In one embodiment the invention relates to the use of a monitoringsystem comprising two or more sensors determining the analyte-relatedsignals based on the same analyte. The method is described below withreference to the use of two collection reservoirs into which theanalyte-containing samples are extracted. However, one of ordinary skillin the art, following the guidance of the present specification, couldadapt this method for use with monitoring systems having one sensor ormore than two sensors used to determine analyte-related signals.

In this exemplary method, two analyte-related signals are obtained fromtwo independent sensors. For example, sensors in contact with extractedsample, comprising the analyte, are used to obtain a signal from eachsample that is related to the analyte amount or concentration in thesubject. Repeated rounds of extraction and sensing provide a series ofsignals. A sensing device may, for example, comprise first and secondsensors, wherein the first sensor is in operative contact with the firstcollection reservoir and the sensing provides signal S^(A) _(j) (where Sis the signal, j is the time interval, for example a measurementhalf-cycle where a full measurement cycle comprises obtaining signalfrom sensor A and sensor B), and A denotes that the signal is fromsensor A), and the second sensor is in operative contact with the secondcollection reservoir and the sensing provides signal S^(B) _(j+1) (whereS is the signal, j+1 is the time interval, for example a measurementhalf-cycle where a full measurement cycle comprises obtaining signalfrom sensor A and sensor B, e.g., a full measurement cycle is(j)+(j+1)), and B denotes that the signal is from sensor B).

Rather than basing the analyte-related measurement solely on S^(A)/S^(B)pairs of signals, analyte-related measurements can be based on a rollingvalue of two (or more) signals in a series. For example, when using twocontiguous signals, a series of rolling average signals may becalculated as follows:(average signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j))/2,  Eqn. 1where (j−1) is the measurement half-cycle previous to j;(average signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1))/2;  Eqn. 2(average signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2))/2,  Eqn. 3where (j+2) is two measurement half-cycles after j; and, for eachprevious equation (Eqn.), wherein each average signal provides ameasurement related to the amount or concentration of analyte in thesubject. Calculation of further average signals at later (e.g., j+3,j+4, etc.) or earlier (e.g., j−1, j−2, etc.) times is accomplishedfollowing the procedure of the examples shown above.

Alternately, the sum or two or more signals may be related to analyteamount or concentration. In this case, a series of rolling values may becalculated, for example, when using two contiguous signals, as follows:(summed signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j));  Eqn. 4(summed signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1)); and  Eqn. 5(summed signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2))  Eqn. 6Calculation of further summed signals at later (e.g., j+3, j+4, etc.) orearlier (e.g., j−1, j−2, etc.) times is accomplished following theprocedure of the examples shown above.

This method can be applied to data obtained using the GlucoWatchbiographer. The GlucoWatch biographer comprises two sensing electrodes,designated “A” and “B”, each in contact with a hydrogel. Current ispassed across A and B for iontophoretic extraction of glucose. Eachhydrogel is in contact with a sensor electrode, which provides signalsrelated to glucose amount or concentration in a sample. After a sampleis transdermally extracted into the hydrogel, the glucose in the samplereacts with the glucose oxidase within the hydrogel to produce hydrogenperoxide. The presence of hydrogen peroxide generates a current at thesensor electrode that is directly proportional to the amount of hydrogenperoxide in the hydrogel. This current provides a signal which can bedetected and interpreted (for example, employing a Mixtures of Expertsalgorithm, see, for example, U.S. Pat. Nos. 6,180,416, 6,326,160, hereinincorporated by reference in their entireties) by an associated systemcontroller to provide a glucose amount or concentration value fordisplay.

A current related to glucose amount or concentration in the sample(i.e., an analyte-related signal) is obtained at each sensor using abiosensor. Current may be converted to charge by integration. In thefirst version of the GlucoWatch biographer, each measurement cycleconsisted of the following: 3 minutes of iontophoresis, followed by a 7minutes of measurement for the first half cycle, then the iontophoresiscurrent polarity was reversed and there was another 3 minutes ofiontophoresis followed by 7 minutes of biosensor measurement. Theglucose was drawn to the cathode, so in the first half cycle the “B”side collected glucose and in the second half cycle the “A” sidecollected glucose. The A and B measurements were averaged as one methodto achieve noise reduction. Accordingly, one hour's worth of datainvolved three full cycles of BA averages: BA, BA, BA (FIG. 1). Usingthe method of the present invention, the averaging is done with a “leapfrog” or rolling average approach, in which the last half cycle of ameasurement becomes the first half cycle of the next measurement. Onehour's worth of measurements can involve six full cycles of BA or ABaverages: BA, AB, BA, AB, BA AB (FIG. 1). Thus the method provides theadvantage of updating measurements more frequently to provide them tothe user. Therefore, in the case of no skip-reading errors, measurementsare reported based on signals from biosensors A and B measurement pairssuch as: BA, AB, BA, AB, and so on.

The configuration of the GlucoWatch biographer included six extractionsper hour, yet only three hourly readings. From an engineeringstandpoint, this represents a sub-optimum number of hourly readings.However, from the standpoint of a person with diabetes, and the numberof readings cannot be said to be a sub-optimum as three hourly readingsrepresent an unprecedented amount of information for subjects using themonitor. One solution to provide more hourly readings is to change thesequence of the GlucoWatch biographer to compute readings morefrequently. The method described above computes six hourly readingscorresponding to the six extractions. Advantages of the methods of thepresent invention for providing more measurement values include, but arenot limited to, the following: allowing tighter screens for providingdata points having minimum error; providing higher time/temporalresolution to more accurately portray data trends; providing a longerwindow for calibration; increasing the probability that a user can gettrend data; and, providing more universal optimization due to largerpool of data.

The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

In one aspect of the present invention, one or more microprocessorsemploy an algorithm comprising one or more of the “rolling value”calculations described above. Typically, such one or moremicroprocessors are components of an analyte monitoring system.

As is apparent to one of skill in the art, various modification andvariations of the above embodiments can be made without departing fromthe spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

B. Interpolation/Extrapolation

In another aspect of the present invention, interpolation and/orextrapolation are used to estimate unusable, missing or error-associatedanalyte-related signals. Such signals may be unusable for a variety ofreasons, typically where an error has been detected that places adetected analyte-related signal in question. Readings with suchassociated errors are typically “skipped.” In the interpolation aspect,one or more previous analyte-related signals and one or more subsequentanalyte-related signals are used to estimate an interveninganalyte-related signal. In the extrapolation aspect, two or moreprevious analyte-related signals are used to estimate a subsequentanalyte-related signal. Interpolation and extrapolation of values arealso employed in another aspect of the present invention that reducesthe incident of failed calibrations, described below. Exemplary methodsare described below with reference to the use of two collectionreservoirs into which the analyte-containing samples are extracted.However, one of ordinary skill in the art, following the guidance of thepresent specification, could adapt this method for use a single sensoror with more than two sensors used to determine analyte-related signals.

The interpolation and/or extrapolation methods of the present inventioncan be applied to single-sensor or multiple-sensor (i.e., two or moresensors) analyte monitoring systems. The following examples illustrateapplication of the methods of the present invention to a two-sensorsystem; however, modification of the method for application to analytemonitoring systems with a different number of sensors is within theability of one of ordinary skill in the art in view of the teachings ofthe present specification.

In one aspect of the present invention, interpolation and/orextrapolation are used to estimate a skipped reading in a series ofreadings. For example, when using the rolling value described above, ifan A or B reading is skipped (e.g., because of associated error), valuessurrounding (interpolation) or preceding (extrapolation) can be used toestimate the skipped value. The estimated value may then be combinedwith adjacent values to provide, for example, an average “A/B” readingor a summed “A/B” reading related to analyte amount or concentration.

Further, interpolation and extrapolation methods may be combined. Forexample, one missing reading may be provided by interpolation and thenthat reading may be used to in the extrapolation of another missingreading.

The present invention includes the use of relationships between thesignals obtained from the sensors to perform interpolation and/orextrapolation of estimated values. For example, in a two sensor system aratio of signals obtained from a first sensor relative to a secondsensor may be employed in such interpolations and/or extrapolations toestimate values. Examples of methods of interpolation and/orextrapolation that can be used to provide estimated values are presentedbelow.

In one embodiment, interpolation and/or extrapolation are used in amethod for reducing the incidence of failed calibration for an analytemonitoring system. Calibration of analyte monitoring systems is oftenperformed using a second, independent device. For example, in the caseof the GlucoWatch biographer a calibration point relative to bloodglucose concentration is provided by the user. The GlucoWatch biographeris put in place on the subject, allowed time to stabilize by performingseveral rounds of sampling and sensing, then the user uses anindependent glucose monitor (for example performing a finger stick andusing an optical detection system) to obtain a blood glucose value at acalibration time point. The calibration blood glucose value is enteredinto the GlucoWatch biographer by the user and the biographer associatesthe blood glucose value with its own sensor determinations of glucoseamount for the same time point.

A series of samples is extracted from the subject using a samplingdevice. The extraction takes place alternately into a first collectionreservoir and then into a second collection reservoir. Each samplecomprises the analyte. In this example, the sampling device comprisesfirst and second collection reservoirs and a measurement cycle refers toextracting into the first collection reservoir, extracting into saidsecond collection reservoir, and sensing analyte in each collectionreservoir. The sensing device is used to obtain a signal from eachsample that is-related to the analyte amount or concentration in thesubject, thus providing a series of signals. In this example the sensingdevice comprises a first sensor (A) and second sensor (B), wherein (1)the first sensor (A) is in operative contact with the first collectionreservoir and the second sensor (B) is in operative contact with thesecond collection reservoir. Also, two consecutive signals comprise ameasurement cycle, and each of the two consecutive signals is half-cyclesignal.

A calibration method is performed to relate analyte amount orconcentration in the subject to signals obtained from the sensors, wherethe calibration method comprises:

(i) obtaining a valid first half-cycle signal S_(j), where a half-cyclesignal S_(j+1), or an estimate thereof, and a half-cycle signal S_(j+2),or an estimate thereof, are both used in the calibration method so thatthe sensor signals correlate to the analyte amount or concentration inthe subject, wherein the calibration method also employs an analytecalibration value that is independently determined;

(ii) providing the analyte calibration value; and

(iii) selecting a conditional statement selected from the groupconsisting of:

(a) if neither the second half-cycle signal S_(j+1) nor the thirdhalf-cycle signal S_(j+2) comprise errors, then S_(j+1) and S_(j+2) areused in the calibration method;

(b) if only the second half-cycle signal S_(j+1) comprises an error,then an estimated signal S^(E) _(j+1) is obtained by determining aninterpolated value using signal S_(j) and S_(j+2), wherein theinterpolated value is S^(E) _(j+1), and S^(E) _(j+1) and S_(j+2) areused in the calibration method;

(c) if only the third half-cycle signal S_(j+2) comprises an error, thenan estimated signal S^(E) _(j+2) is obtained by determining anextrapolated value using signal S_(j) and S_(j+1), wherein theextrapolated value is S^(E) _(j+2), and S_(j+1) and S^(E) _(j+2) areused in the calibration method; and

(d) if both the second half-cycle signal S_(j+1) and the thirdhalf-cycle signal S_(j+2) comprise errors, then return to (i) to obtaina new, valid half-cycle signal S_(j) from a later measurement half-cyclethan the measurement half-cycle from which the previous, valid S_(j)half-cycle signal was obtained.

This method can be applied to data obtained using the GlucoWatchbiographer. When used in the GlucoWatch biographer this method ofprocessing data reduces the incident of failed calibrations. Thereduction was achieved by allowing calibrations in the presence of a“skip error” (i.e., when a signal is skipped) for only one of the two10-minute sensor half-cycles at calibration. In this case, the sensorsignal during the skipped period is estimated by a interpolation if itis the first half-cycle or extrapolation if it is the second half-cycle.

In one embodiment of the present invention, the calibration methodbegins when a non-skipped half-cycle has been successfully completed inorder to attempt calibration. Therefore, a skipped half-cycleimmediately before the expected opening of a “calibration window” causesthe window to not open, or be “suppressed”, until a half-cycle free ofskip errors is completed. A calibration window refers to a period oftime in which a user enters an independently determined analytecalibration value. In this embodiment of the invention, an un-skippedhalf-cycle is a gating requirement for the calibration process to beinitiated. However, the method allows that estimated signals may beprovided by interpolation or extrapolation should they be needed.

One benefit of the processing method is a decrease in abortedcalibrations. Experiments performed in support of the present inventionshowed a reduction of more than 50% in aborted calibrations (not due toout-of-range entry) when the methods described herein were employed.When out-of-range entries were considered, a 21% decrease in failedcalibrations was observed.

The processing method described herein for reducing the incidence offailed calibrations used three half-cycles, S_(j), S_(j+1),S_(j+2) atcalibration. In the absence of skip errors, signals from S_(j+1) andS_(j+2) were used to complete calibration. If either S_(j+1) and S_(j+2)(but not both) had a skip error, then a method of interpolations orextrapolations was invoked to estimate the signal at the skippedhalf-cycle. If both S_(j+1) and S_(j+2) contained skip errors, then afailed calibration resulted.

The interpolation method was invoked when calibration half-cycle S_(j+1)had a skip error while S_(j) and S_(j+2) did not. Note that S_(j) andS_(j+2) were from the same sensor (A or B), while S_(j+1) was fromanother sensor. One interpolation method, shown below in Eqn. 7A throughEqn. 7D, simply assumes that the estimated S_(j+1) lies at a point onthe line between S_(j) and S_(j+2), whose distance is related to thetime interval between the points, with a correction for differencesbetween Sensors A and B using an “AB ratio.” In one embodiment of thepresent invention, the same AB ratio is used for interpolation and/orextrapolation regardless of the sensor source (i.e., A or B) of thesignals being used to calculate the estimated (i.e.,interpolated/extrapolated) signal value. In another embodiment of thepresent invention the form of the AB ratio used for interpolation and/orextrapolation depends on the sensor source (i.e., A or B) of the signalsbeing used to calculate the estimated (i.e., interpolated/extrapolated)signal value. A further discussion of the AB ratio is presented below.An exemplary interpolation method follows here for a two sensor systemwhere a different forms of the AB ratio are used depending on the sourceof the signals being used in the calculation.

In the situation where both S_(j) and S_(j+2) are signals from the Bsensor (S^(B) _(j) and S^(B) _(j+2)), and S_(j+1) is being estimated forthe A sensor signal (S^(AE) _(j+1)), interpolation Eqn. 7A may beemployed as follows: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left\{ {S_{j}^{B} + {\left( {S_{j + 2}^{B} - S_{j}^{B}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\mspace{14mu}\text{7A}}\end{matrix}$

wherein t is the time interval, for example, measurement half-cyclet_(j), one subsequent half-cycle, t_(j+1), or two subsequent half-cyclest_(j+2). When the points are equally spaced, that is when2(t_(j+1)−t_(j))=(t_(j+2)−t_(j)), then Eqn. 7A reduces to the followingEqn. 7B: $\begin{matrix}{S_{j + 1}^{AE} = {\frac{A}{B}\left( \frac{S_{j}^{B} + S_{j + 2}^{B}}{2} \right)}} & {{Eqn}.\mspace{14mu}\text{7B}}\end{matrix}$

In the situation where both S_(j) and S_(j+2) are signals from the Asensor (S^(A) _(j) and S^(A) _(j+2)), and S_(j+1) is being estimated forthe B sensor signal (S^(BE) _(j+1)), interpolation Eqn. 7C may beemployed as follows: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left\{ {S_{j}^{A} + {\left( {S_{j + 2}^{A} - S_{j}^{A}} \right)\frac{\left( {t_{j + 1} - t_{j}} \right)}{\left( {t_{j + 2} - t_{j}} \right)}}} \right\}}} & {{Eqn}.\mspace{14mu}\text{7C}}\end{matrix}$

When the points are equally spaced, that is when2(t_(j+1)−t_(j))=(t_(j+2)−t_(j)), then Eqn. 7C reduces to the followingEqn. 7D: $\begin{matrix}{S_{j + 1}^{BE} = {\frac{B}{A}\left( \frac{S_{j}^{A} + S_{j + 2}^{A}}{2} \right)}} & {{Eqn}.\mspace{14mu}\text{7D}}\end{matrix}$

The extrapolation method was invoked when calibration half-cycle S_(j+2)had a skip error while S_(j) and S_(j+1) did not. Note that S_(j) andS_(j+1) are from different sensors (A and B), while S_(j+2) is from thesame sensor as S_(j). The extrapolation method, shown in the Eqn. 8Athrough Eqn. 8D, assumes the extrapolated point is on a line connectingS_(j) and S_(j+1), using a correction for differences between sensors Aand B, and estimates a value for S_(j+2). As noted above, a single ABratio may be employed or the AB ratio may take different forms dependingon the sensor source of the signals. Further discussion of the AB ratiois presented below. An exemplary interpolation method follows here for atwo-sensor system where different forms of the AB ratio are useddepending on the source of the signals being used in the calculation.

In the situation where S_(j) is signal from sensor A (S^(A) _(j)) andS_(j+1) is signal from B sensor (S^(B) _(j+1)), and S_(j+2) is beingestimated for the A sensor signal (S^(AE) _(j+2)), extrapolation Eqn. 8Amay be employed as follows: $\begin{matrix}{S_{j + 2}^{AE} = {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} + \left\lbrack {\left\{ {{\frac{A}{B}\left( S_{j + 1}^{B} \right)} - S_{j}^{A}} \right\}\frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\mspace{14mu}\text{8A}}\end{matrix}$

When the points are equally spaced, that is when(t_(j+2)−t_(j+1))=(t_(j+1)−t_(j)), then Eqn. 8A reduces to the followingEqn. 8B: $\begin{matrix}{S_{j + 2}^{AE} = {{2\frac{A}{B}S_{j + 1}^{B}} - S_{j}^{A}}} & {{Eqn}.\mspace{14mu}\text{8B}}\end{matrix}$

In the situation where S_(j) is signal from the B sensor (S^(B) _(j))and S_(j+1) is signal from the A sensor (S^(A) _(j+1)), and S_(j+2) isbeing estimated for the B sensor signal (S^(BE) _(j+2)), extrapolationEqn. 8C may be employed as follows: $\begin{matrix}{S_{j + 2}^{BE} = {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} + \left\lbrack {\left\{ {{\frac{B}{A}\left( S_{j + 1}^{A} \right)} - S_{j}^{B}} \right\}\frac{\left( {t_{j + 2} - t_{j + 1}} \right)}{\left( {t_{j + 1} - t_{j}} \right)}} \right\rbrack}} & {{Eqn}.\mspace{14mu}\text{8C}}\end{matrix}$

When the points are equally spaced, that is when(t_(j+2)−t_(j+1))=(t_(j+1)−t_(j)), then Eqn. 8C reduces to the followingEqn. 8D: $\begin{matrix}{S_{j + 2}^{BE} = {{2\frac{B}{A}S_{j + 1}^{A}} - S_{j}^{B}}} & {{Eqn}.\mspace{14mu}\text{8D}}\end{matrix}$

The interpolation and extrapolation methods described above use arelationship between the signals of sensor A and B. One method ofdetermining this relationship is to calculate a weighted average using asmoothing protocol. Smoothing methods that may be employed include, butare not limited to, (i) taking a basic average of all available ratios,(ii) initializing the processing method with the ratio at some specificpoint, (iii) trend methods, and (iv) methods including exponentialcomponents. One exemplary smoothing protocol useful in the practice ofthe present invention is the Holt-Winters smoothing (examplescorresponding to the ratios used above are shown in Eqn. 9A and Eqn.9B). $\begin{matrix}{\left( \frac{A}{B} \right)_{s,i} = {{w\left( \frac{A}{B} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{A}{B} \right)_{s,{i - 1}}}}} & {{Eqn}.\mspace{14mu}\text{9A}} \\{\left( \frac{B}{A} \right)_{s,i} = {{w\left( \frac{B}{A} \right)}_{i} + {\left( {1 - w} \right)\left( \frac{B}{A} \right)_{s,{i - 1}}}}} & {{Eqn}.\mspace{14mu}\text{9B}}\end{matrix}$

In Eqn. 9A and Eqn. 9B, (A/B)_(s,i) and (B/A)_(s,i)refer to “smoothed”AB ratios for measurement cycle i, (A/B)_(i) and (B/A)_(i), refer to theAB ratio for measurement cycle i, and (A/B)_(s,i−1) and (B/A)_(s,i−1),refer to the smoothed AB ratio from the previous measurement cycle i−1.In the Holt-Winters smoothing presented above, the determination of thesmoothed AB ratio depends on the adjustable parameter w (a weightingfactor). Experiments performed in support of the present invention werecarried out on the sensor signals at early times for the GlucoWatchbiographer. Predictions of sensor signal were generated at all potentialpoints with both interpolation and extrapolation. For those points wherean actual sensor signal was available, a relative error was calculated.The mean relative absolute relative error (MARE) was found for eachsensor for each method at each smoothing weight. Based on an initialanalysis a smoothing weight 0.7 (i.e., 70%) was chosen. Other smoothingweights may be employed where w is a smoothing factor and represents anumerical, percentage value between and inclusive of 0 to 100%, where wis represented by a fraction between and inclusive of 0 to 1.

Experiments performed in support of the present invention suggest that areduction of failed calibrations is the main impact of the newprocessing method, thus resulting in better “usability” of theGlucoWatch biographer, in the sense that the number of finger-pricksrequired for calibration (i.e., the analyte calibration valuedindependently obtained) is greatly reduced.

The smoothed AB ratios described above were used in the interpolationand extrapolation of skipped half-cycle signals, e.g., at calibration.Smoothing is not essential and in some applications a fixed AB ratio maybe employed. It was initially assumed that the smoothed B/A ratio wouldbe reciprocal of the smoothed A/B ratio. When this is the case a singleAB ratio may be used. However, experiments performed in support of thepresent invention that employed the GlucoWatch biographer indicated thatthe smoothed B/A ratio was not mathematically equivalent to thereciprocal of the smooth A/B ratio and that in some applications use ofseparate smoothed A/B and B/A ratios (as shown above) provides morereliable results for the interpolation and extrapolation of bothsensors, e.g., at calibration.

The processing methods discussed above are optimal when both A/B and B/Aratios are calculated separately for each consecutive non-skip B cathodeand A cathode half-cycles. As new A/B and B/A ratios are calculated fornews cycles, the most recent one is smoothed with the previous one. Oneexemplary smoothing method is performed according to the-followinggeneral equation (Eqn. 10):R _(i) ^(s) =wR _(i)+(1−w)R _(i−1) ^(s)  Eqn. 10

wherein, R_(i) is the A/B or B/A ratio for a i^(th) measurement cycle,R^(S) _(i) is smoothed R for a i^(th) measurement cycle, and w is asmoothing factor and represents a numerical, percentage value betweenand inclusive of 0 through 100%, where w is represented by a fractionbetween and inclusive of 0 through 1, and R^(S) _(i−1) is a smoothedratio for the (i−1)^(th) measurement cycle, wherein the i^(th)measurement cycle is composed of first and second half-cycles and thesecond half-cycle value of the i^(th) measurement cycle precedes S_(j).A single R^(S) _(i)may be used or more than one such ratio may beemployed.

The smoothed ratios of A/B and B/A are stored and used for aninterpolation and extrapolation of skipped cycle intervals, e.g., duringcalibrations.

In order to compare interpolation and/or extrapolation estimates using asingle AB ratio versus methods employing separate A/B and B/A ratios, acalibration cycle with interpolation and extrapolation was simulatedwithout specifying the identity of the A and B sensors. Then each sensorwas arbitrarily assigned to A or B. When a single AB ratio wasmaintained (single AB ratio method), the interpolation an extrapolationresults are dependent on which sensor was assigned A and which sensorwas assigned B. When separate A/B and B/A ratios were maintained(separate A/B and B/A ratio method), the interpolation and extrapolationresults were identical when the A and B sensor assignments wereswitched. This simulation suggested that calculating separate A/B andB/A ratios was more accurate than using a single AB ratio. However,using a single AB ratio still provides an efficacious means forcalculating estimated values.

Experiments performed in support of the present invention suggest thatminor inconsistencies in interpolation and extrapolation calculationsthat resulted from the use of a single smooth AB ratio for both the Aand B sensors was eliminated when separate smoothed A/B and B/A ratiosare maintained.

In this modification of methods of making interpolation and/orextrapolation estimates of the present invention, the A/B ratio and B/Aratio can, for example, store ratios on the A and B signal integralsfrom the later part of the equilibration period. They are then used asneeded interpolation and/or extrapolation of skipped signals.

In the context of the methods of the present invention for reducing thenumber of failed calibrations applied to the GlucoWatch biographer, anacceptable A/B (and B/A) ratio is generated if there is a least one pairof consecutive non-skipped A and B signals prior to this first goodhalf-cycle signal that begins the calibration. Until good A/B and B/Aratios are available, a biographer employing the methods of the presentinvention does not open a calibration window and calibration will not beperformed.

If more than one pair is available, then a smoothing technique may beused to obtain a rolling value (for example, as shown in Eqn. 10,above):R _(i) ^(s) =wR _(i)+(1−w)R _(i−1) ^(s)  Eqn. 10

where R is the A/B ratio or B/A ratio, the smoothing factor w representsa numerical, percentage value between and inclusive of 0 to 100%, wherew is represented by a fraction between and inclusive of 0 to 1. Tomaximize consistency with signals from the A and B sensors and forcalculation of skipped integrals, separate A/B and B/A ratios aremaintained for use in the ratio smoothing equation. The AB and BA ratiosmay be maintained after calibration for use, for example, duringrecalibration or for interpolation and/or extrapolation of later missingvalues. The ratios may be updated after each pair of consecutivenon-skipped A and B signals.

As discussed above, the interpolation method is invoked when calibrationhalf-cycle S_(j+1) has a skip error while S_(j) and S_(j+2) do not. Notethat S_(j) and S_(j+2) are from the same sensor (A or B), while S_(j+1)is from the other sensor. The interpolation method, shown above, assumesthat S_(j+1) lies at the vertical midpoint between S_(j) and S_(j+2),and is then corrected for Sensor A to B differences by as follows: wheninterpolating for the A sensor (S_(j+1) is the A sensor), the A/B ratiois used. When interpolating for the B sensor (S_(j+1) is the B sensor),the B/A ratio is used.

Also as discussed above, the extrapolation method is invoked whencalibration half-cycle S_(j+2) has a skip error while S_(j) and S_(j+1)do not. Note that S_(j) and S_(j+1) are from different sensors (A andB), while S_(j+2) is from the same sensor as S_(j). The extrapolationmethod, shown above, makes essentially the same assumptions as theinterpolation method, but solves for S_(j+2). When extrapolating for theA sensor the Ratio_(ab) factor is equal to the A/B ratio. Whenextrapolating for B sensor the Ratio_(ab) factor is equal to the B/Aratio.

The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

Further, experiments performed in support of the present invention thatutilize the GlucoWatch biographer have indicated that skipped signalstend to occur in clusters. Accordingly, one aspect of the presentinvention comprises waiting for an unskipped (i.e., error free or goodsignal) half-cycle signal before initiating a calibration sequence(e.g., before opening a calibration window inviting the user to providean independently determined analyte calibration value). Such anindependently determined analyte calibration value may be obtained, forexample, by using a traditional blood glucose measuring device, e.g.,blood glucose amounts as determined using a OneTouch® (Johnson &Johnson, New Brunswick, N.J.) blood glucose monitoring device.

Although the above-methods have been exemplified for two sensors, themethods can be applied to single-sensor or multiple-sensor (i.e., two ormore sensors) devices by one of ordinary skill in the art in view of theteachings of the present specification.

The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

In one aspect of the present invention, one or more microprocessorsemploy an algorithm comprising one or more of the interpolation,extrapolation, and/or A/B ratio calculations described above. Typically,such one or more microprocessors are components of an analyte monitoringsystem.

As is apparent to one of skill in the art, various modification andvariations of the above embodiments can be made without departing fromthe spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

C. Integral Splitting

One extension of using “rolling values” to increase frequency ofreadings is to use subsets of each trapezoidal integral (i.e., integralsplitting) to update the signal after each biosensor current reading istaken. For example, when each analyte-related signal is represented byan integral over time, rolling analyte measurement values may beobtained by integral splitting. In this way, the readings are reportedto the user as new information is obtained. This allows readings to begiven as often as current readings can be taken, which may be, forexample, as often as more than once per second. FIGS. 3 and 4 illustratethe concept of integral splitting. In FIGS. 3A to 3C, three differentread frequencies schemes are shown that range from serial pairedmeasurements (AB, AB, AB; FIG. 3A), to a “rolling value” measurement(AB, BA, AB, BA; FIG. 3B), and finally an “integral split” measurement(FIG. 3C), where readings are provided most frequently. In thisnomenclature, the numbers in parentheses are “ranges” of trapezoidalsegments used for each measurement (e.g. A2(2–N)A3(1)B2 means to use theentire B2 integral, along with the first segment of the A3 and fromsegment 2 to N of A2 with N being the total number of segments)—theseexpressions are not mathematical formulae.

In FIG. 4A, an example is shown of how the newest trapezoidal segmentreplaces the oldest one as a new current value is taken. In thisexample, the eighth sensor reading completes the seventh trapezoidalsegment of Integral A3. This segment A3(7) replaces A2(7), so that thefinal signal used for calculation changes from A2(7–N)A3(1–6)B2 toA2(8–N)A3(1–7)B2. FIG. 4B is an illustration of the increase in readingfrequency for various measurement methods described above (e.g.,integral splitting and rolling values relative to serial paired).

The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

In one aspect of the present invention, one or more microprocessorsemploy an algorithm comprising integral splitting calculations asdescribed above to provide readings as often as current readings can betaken. Typically, such one or more microprocessors are components of ananalyte monitoring system.

As is apparent to one of skill in the art, various modification andvariations of the above embodiments can be made without departing fromthe spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

D. Recalibration Methods

(i) Optional Recalibrations

The following recalibration methods are described with reference to theGlucoWatch biographer, however, in view of the teachings of the presentspecification one of ordinary skill in the art can apply theserecalibration methods to any analyte monitoring system that provides aseries of analyte readings dependent on obtaining a calibration value.As described above, the GlucoWatch biographer is an exemplary analytemonitoring system where the analyte of interest is glucose. In theGlucoWatch biographer, a user is able to recalibrate the GlucoWatchbiographer at any time if, for example, the GlucoWatch biographerreadings are inconsistent with the user's physical symptoms or if theuser receives a number of SKIP messages (i.e., messages relating to datathat do not conform to predetermined criteria (for example,error-associated criteria as described in U.S. Pat. No. 6,233,471,herein incorporated by reference). In the GlucoWatch biographer, if theuser decides to recalibrate the GlucoWatch biographer and isunsuccessful for any reason (for example, due to SKIP errors orout-of-range entry), the previously entered calibration is terminatedand the GlucoWatch biographer reverts to an uncalibrated state. TheGlucoWatch biographer is then only able to generate glucose readings ifthe re-calibration entry is successful. This situation can potentiallylead to long periods of time in which the user receives no glucosereadings. This situation is illustrated in FIG. 5.

A useful modification of the above-described recalibration feature isthat during a failed optional recalibration, the glucose monitoringsystem can continue to generate glucose readings using the previouslyaccepted calibration value while the glucose monitoring system assessesthe entered re-calibration value. Once the entered, re-calibration valueis accepted, the glucose monitoring system begins to generate glucosereadings using the new calibration value. This situation is illustratedin FIG. 6.

(ii) Consecutive Skipped Measurements and Required Re-Calibration

The following recalibration methods are described with reference to theGlucoWatch biographer, however, in view of the teachings of the presentspecification one of ordinary skill in the art can apply theserecalibration methods to any analyte monitoring system that provides aseries of analyte readings dependent on obtaining a calibration value.If the GlucoWatch biographer produces six consecutive skipped readings,then the GlucoWatch biographer aborts the sequence and the user mustchange the AutoSensor and perform a warm-up and calibration periodbefore the GlucoWatch biographer will produce glucose measurementsagain. The sequence is aborted and the measurement period is terminatedearly, for example, to safeguard against potentially inaccuratereadings. When the sequence aborts, the GlucoWatch biographer sounds adistinctive beep, and an error message is displayed. Additional researchhas shown that the number of consecutive skips may be increased from sixto eighteen without reducing the safety or effectiveness of the device.

In addition to increasing the number of allowable consecutive skips,rather than aborting the sequence, the user may be asked to re-calibratethe device after, for example, eighteen consecutive skips. Aftereighteen consecutive skips, the analyte monitoring system removes theoriginal calibrations value and requests a new conventional meter bloodglucose measurement. This is termed “Required Re-Calibration. ” DuringRequired Re-Calibration, the user engages the same calibration processthat is normally performed at the end of a warm-up period. For thisRe-Calibration method, the calibration integrity checks (interpolationand/or extrapolation, suppression) are applied to re-calibrating theanalyte monitoring device.

Accordingly, for an analyte monitoring system that requires calibration,a number of consecutive skips can be determined that still allows theanalyte monitoring system to provide safe and effective readings. Afterthe number of consecutive skips is met or exceeded one or moremicroprocessors of the analyte monitoring system may be programmed toforce a required recalibration.

The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

In one aspect of the present invention, one or more microprocessorsemploy an algorithm comprising programmed instructions to execute theabove described recalibration methods. Typically, such one or moremicroprocessors are components of an analyte monitoring system.

As is apparent to one of skill in the art, various modification andvariations of the above embodiments can be made without departing fromthe spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

6. Methods of Providing an Analyte Concentration-Related Alert

Following here two approaches are described to provide an analyteconcentration-related alert when an analyte level falls above or belowpredetermined thresholds or outside of a predetermined range ofreference values: gradient methods and predictive algorithm methods.These methods provide for predicting an analyte concentration-relatedevent in a subject being monitored for levels of a selected analyte.Exemplary analyte concentration-related alerts include, but are notlimited to, a “down alert,” that is an alert when an analyte levelsfalls below a predetermined value or range of values (e.g., ahypoglycemic alert), and an “up alert,” that is, an alert is providedwhen an analyte level falls above a predetermined value or range ofvalues (e.g., a hyperglycemic alert).

The gradient method employs the current and the past analyte monitoringsystem reading and determines the rate of decline and/or increase. Therate of change of the analyte (e.g., a rate of change of the analyte inthe direction of decreasing amount of analyte) in the subject is thenused to determine whether to alert the subject or not. One limitation ofthis method in the context of glucose monitoring is that, when thismethod is used alone for the prediction of, e.g., a hypoglycemic event,it would trigger the down alert even at very high blood glucose levelswhen the rate of decline exceeded the acceptable, predetermined rate ofchange.

The predictive algorithm method uses a predictive algorithm to predictthe next analyte reading based on previously obtained analyte readings(e.g., obtained using the GlucoWatch biographer). Based on the value ofthis predicted reading relative to predetermined threshold values orrange of values, the analyte concentration-related alert would or wouldnot be triggered. The gradient method and the predictive algorithmmethod may be combined. One preferred embodiment is discussed belowwhere the gradient method is combined with the predictive algorithmmethod. Further, such a combination method may be combined withindividual predictors (such as, skin conductivity and temperature).

A. The Gradient Method

Several models for the determination of a gradient (i.e., the rate ofchange) are as follows: $\begin{matrix}{{{{Model}\mspace{14mu} A\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 1})}}{\Delta\; t}\mspace{11mu}\left( {{concentration}/{time}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}}\;} \\{{{Model}\mspace{14mu} B\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 1})}}{y_{({n - 1})}\Delta\; t}\mspace{11mu}\left( {{fractional}\mspace{14mu}{{change}/{time}}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 1})}} \right)}} \\{{{Model}\mspace{14mu} C\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 2})}}{\Delta\; t}\mspace{11mu}\left( {{concentration}/{time}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\mspace{14mu} D\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 2})}}{y_{({n - 2})}\Delta\; t}\mspace{11mu}\left( {{fractional}\mspace{14mu}{{change}/{time}}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 2})}} \right)}} \\{{{Model}\mspace{14mu} E\text{:}\mspace{11mu}{{Average}\left\lbrack {\frac{y_{(n)} - y_{({n - 1})}}{\Delta\; t_{1}},\frac{y_{({n - 1})} - y_{({n - 2})}}{\Delta\; t_{2}},\frac{y_{(n)} - y_{({n - 2})}}{\Delta\; t_{3}}} \right\rbrack}}\mspace{11mu}} \\{\mspace{110mu}{\left( {{concentration}/{time}} \right);}}\end{matrix}$

where

Δt₁=(t_((n))−t_((n−1))), Δt₂=(t_((n−1))−t_((n−2))), andΔt₃=(t_((n))−t_((n−2))). In this model, the average is of all threevalues shown in the brackets.${{Model}\mspace{14mu} F\text{:}\mspace{11mu}\frac{y_{(n)} - y_{({n - 3})}}{y_{({n - 3})}\Delta\; t}\mspace{11mu}\left( {{fractional}\mspace{14mu}{{change}/{time}}} \right)};{{{where}\mspace{14mu}\Delta\; t} = \left( {t_{(n)} - t_{({n - 3})}} \right)}$

In the above models, y_(n) stands for an analyte reading at time pointt_((n)), y_((n−1)) an analyte reading at time point t_((n−1)) (i.e., theprevious reading to y_(n)), y_((n−2)) an analyte reading at time pointt_((n−2)) (i.e., the reading previous to y_((n−1))), y_((n−3)) ananalyte reading at time point t_((n−3)) (i.e., the reading previous toy_((n−2))). Each of the above methods give a rate of change. Models A,C, and E give concentration change per time interval, for example, theunits may be mg/dL/minute or mmol/L/minute when y is a glucose reading.Models B, D, and F give a fractional change per time interval (e.g.,percentage change in the glucose reading per minute). When using agradient method a threshold of an acceptable rate of change is selected(for example, based on experimental data and/or acceptable ranges ofmeasurement values).

In one embodiment, a microprocessor employs an algorithm comprising theselected model and calculates the rate of change (e.g., in the indicatedunits). The microprocessor then employs an algorithm to compare thecalculated rate of change to a predetermined acceptable rate of change.If the calculated rate of change differs significantly from theacceptable rate of change then the microprocessor triggers the analytemonitoring system to provide an alert to the user. For example, applyingModel D to glucose readings in order to predict a hypoglycemic event anacceptable rate of change may be established as a decrease of 1.75%/minfor Δt=20 min. If the rate of change exceeds a decrease of 1.75%/min forΔt=20 min then the subject is alerted to this fact (e.g., by an audiblealert and/or a prompt on the user interface).

Typically when employing the gradient models, to provide a low-analytealert (e.g., hypoglycemic event alert) the calculated rate of change isnegative and less than the predetermined threshold rate of change (e.g.,a calculated rate of change of negative 2%/min for Δt=20 minutes is lessthan the threshold value of negative change of 1.75%/min for Δt=20 min);and/or to provide a high-analyte alert (e.g., hyperglycemic event alert)the calculated rate of change is positive and greater than thepredetermined threshold rate of change (e.g., a calculated rate ofchange of 2%/min for Δt=20 minutes is greater than the threshold valueof change of 1.75%/min for Δt=20 min). Alternatively, absolute values ofthe calculated and threshold rates of change may be used for comparison.In this case, an alert is provided when the absolute value of thecalculated rate of change is greater than the absolute value ofpredetermined threshold rate of change.

In addition to the above-described gradient models, a number of othermodels can be employed in a gradient method, including, but not limitedto, use of a regression model to determine the gradient, using, forexample, a best-fit function.

B. The Predictive Algorithm Method

One predictive algorithm method (Eqn. 11) has been previously describedfor use in time-series predictions (U.S. Pat. No. 6,272,364, hereinincorporated by reference in its entirety). Several other predictivealgorithm methods follow here as well. $\begin{matrix}{y_{({n + 1})} = {y_{(n)} + {\alpha\left( {y_{(n)} - y_{({n - 1})}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{(n)} - {2y_{({n - 1})}} + y_{({n - 2})}} \right)}}} & {{Eqn}.\mspace{14mu} 11} \\{y_{({n + 1})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 1})} - t_{n}} \right)}}} & {{Eqn}.\mspace{14mu} 12} \\{y_{({n + 1})} = {{\frac{5}{2}y_{(n)}} + {{- 2}\left( y_{({n - 1})} \right)} + {\frac{1}{2}\left( y_{({n - 2})} \right)}}} & {{Eqn}.\mspace{14mu} 13} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 2})}} \right)}{\left( {t_{n} - t_{({n - 2})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\mspace{14mu} 14} \\{y_{({n + 2})} = {y_{(n)} + {\frac{\left( {y_{(n)} - y_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} & {{Eqn}.\mspace{14mu} 15}\end{matrix}$

In these equations, the methods calculate the predicted value of avariable y (e.g., concentration of analyte) at time t_(n+1) (or t_(n+2),as indicated) as a function of that variable at the current time t_(n),as well as at a previous time or times, e.g., t_(n−1) and/or t_(n−2)).In these equations, y_((n+1)) and y_((n+2)) are predicted values ofvariable y at time points (n+1) and (n+2), respectively, y_((n)),y_((n−1)), y_((n−2)) are analyte-related values at times (n), (n−1), and(n−2), respectively, t_((n−2)), t_((n−1)), t_((n)), t_((n+1)),t_((n+2)), are time points at times (n−2), (n−1), (n), (n+1) and (n+2),respectively. In Eqn. 11, α is an empirically determined weighting valuethat is typically a real number between 0 and 1. Each of the abovemethods provides a predicted analyte value, for example, an amount orconcentration (e.g., the units may be mg/dL (milligrams of glucose perdeciliter) or mmol/L when y is a glucose reading). When using apredictive algorithm thresholds of an acceptable range for analyteamount or concentration are selected (for example, based on experimentaldata and/or acceptable ranges of measurement values). High thresholdvalues may be selected (e.g., a glucose value that is consideredhyperglycemic for a subject), low threshold values may be selected(e.g., a glucose value that is considered hypoglycemic for a subject),and/or an acceptable range of values with an associated error may alsobe employed.

In one embodiment, one or more microprocessors employ an algorithmcomprising the selected predictive algorithm and calculates thepredicted value (e.g., in the indicated units). The microprocessor thenemploys an algorithm to compare the predicted value to the thresholdvalue(s). If the predicted value falls above a high threshold, below alow threshold, or outside of a predetermined range of values, then themicroprocessor triggers the analyte monitoring system to provide analert to the user.

When the analyte being monitored is glucose and glucose readings areprovided by a glucose monitoring device (e.g., the GlucoWatchbiographer) y_(n) corresponds to GW_(n), a glucose value in the subjectat time t_(n). Further, for prediction of glucose values when using Eqn.11, α typically equals 0.5.

Eqn. 11 predicts the next analyte value (y_((n+1))) based on the currentanalyte value (y_(n)), and two previous analyte values y_((n−1)) andy_((n−2)), wherein the weight of the effect of y_((n−2)) is determinedby the weighting factor α. Eqn. 12 predicts the next analyte value(y_((n+1))) based on the current analyte value (y_(n)), a previousanalyte value y_((n−1)), and time intervals associated with the times atwhich the analyte values are determined (for (y_(n)) and y_((n−1))) orpredicted (y_((n−1))). Eqn. 13 predicts the next analyte value(y_((n+1))) based on the current analyte value (y_(n)), and two previousanalyte values y_((n−1)) and y_((n−2)). Eqn. 14 predicts an analytevalue(y_((n+2))) at two time points after t_(n) based on the currentanalyte value (y_(n)), a previous analyte value y_((n−2)), and timeintervals associated with the times at which the analyte values aredetermined (for (y_(n)) and y_((n−2))) or predicted (y_((n+2))). Eqn. 15predicts an analyte value(y_((n+2))) at two time points after t_(n)based on the current analyte value (y_(n)), a previous analyte valuey_((n−1)), and time intervals associated with the times at which theanalyte values are determined (for (y_(n)) and y_((n−1))) and predicted(y_((n+2))).

As noted above, when employing the above predictive algorithms, analert/alarm can be used to notify the subject (or user) if the predictedvalue is above/below a predetermined threshold. For example, in thesituation where glucose is the analyte being monitored, a low thresholdof greater than 80 mg/dL may be selected for a particular subject.Accordingly predicted glucose values (obtained by using any of the abovepredictive algorithms) of 80 or less may trigger an alert to thesubject. Typically low threshold values for glucose are between about50–100 mg glucose per dL blood and high threshold values are betweenabout 200–300 mg glucose per dL blood.

C. Combined Approach

In one embodiment of the present invention to provide an analyteconcentration-related alert when an analyte level falls above or belowpredetermined thresholds or outside of a predetermined range ofreference values, an approach combining the above-described gradientmethod and predictive algorithm method is employed. In this embodimentof the present invention, rate of change thresholds are determined aswell as analyte thresholds (or range of values). Generally, a predictivealgorithm is chosen which provides a predicted analyte value at a futuretime point. The predicted value is compared to the threshold value forthe alert. If the predicted value exceeds the threshold value, then therate of change of the analyte is evaluated. If the rate of change of theanalyte level surpasses a predetermined threshold (or falls outside of arange of values) then an analyte concentration-related alert is providedto the subject in whom the analyte levels are being monitored. Of coursethe order of these two comparisons (i.e., predicted value and rate ofchange) may be reversed, for example, where the rate of change isevaluated first and then the predicted value is evaluated.

For example, in the context of providing an alert for a futurehypoglycemic event, a “low alert threshold” is selected. Such athreshold is typically user selected and falls in the range of about 50mg/dL (glucose/blood volume) to about 100 mg/dL. Additionally, one ormore microprocessors of the monitoring system may be programmed tomodify the user the selected threshold by, for example, adding apredetermined value to the selected threshold (for example, if athreshold of 80 mg/dL is selected a program of the monitoring system mayadd 10 mg/dL to the threshold, resulting in a low alert threshold of 90mg/dL).

A predictive algorithm is chosen (e.g., based on Eqn. 15, above)$\begin{matrix}{\left. {{GW}_{({n + 2})} = {{GW}_{(n)} + {\frac{\left( {{GW}_{(n)} - {GW}_{({n - 1})}} \right)}{\left( {t_{n} - t_{({n - 1})}} \right)}*\left( {t_{({n + 2})} - t_{n}} \right)}}} \right).} & {{Eqn}.\mspace{14mu} 16}\end{matrix}$

When the current glucose value, e.g., as determined by the GlucoWatchbiographer, is equal to or less than a predetermined value thepredictive algorithm is invoked to predict a glucose value at a futuretime point (for Eqn. 16 that would be n+2). Simultaneously, orsequentially, a gradient method is employed to determine the rate ofchange of the glucose values, for example, using Model B above where yequals the glucose value as determined by the GlucoWatch biographer, andthe threshold fractional change/time (e.g., %/min) is defined, forexample, as negative 5%/10 minutes:${Model}\mspace{14mu} B^{\prime}\mspace{14mu}{\frac{{GW}_{(n)} - {GW}_{({n - 1})}}{{GW}_{({n - 1})}\Delta\; t}.}$

The rate of change as determined by the gradient method is compared to athreshold value or range of threshold values. If the glucose valuepredicted by the predictive algorithm is less than or equal to thepredetermined low threshold value, and the rate of change is negativeand less than the predetermined threshold rate of change then an alertis provided to the subject in anticipation of a hypoglycemic event.

The present invention includes, but is not limited to, methods,microprocessors programmed to execute the methods, and monitoringsystems (comprising, for example, a sampling device, a sensing device,and one or more microprocessors programmed to control, for example, (i)a measurement cycle utilizing the sampling and sensing devices, and (ii)data gathering and data processing related to the methods of the presentinvention).

In one aspect of the present invention, one or more microprocessorsemploy an algorithm comprising programmed instructions to execute theabove described combined methods for providing an analyte-concentrationrelated alert. Typically, such one or more microprocessors arecomponents of an analyte monitoring system.

In one aspect of the present invention, the rolling values describedabove are employed as the measurement data points in the “analyteconcentration-related” alert methods. As noted above, the rolling valuemethod of the present invention provides for more frequent updating andreporting of analyte measurement values. In a further aspect of thepresent invention interpolation and/or extrapolation methods areemployed to provide missing or error-associated signals in the series ofanalyte-related signals. As discussed above, one or more microprocessorsmay be programmed to execute the calculations associated with a rollingvalue method and/or an analyte-concentration related alert method.

As is apparent to one of skill in the art, various modification andvariations of the above embodiments can be made without departing fromthe spirit and scope of this invention. Such modifications andvariations are within the scope of this invention.

1. A method of increasing the number of analyte measurement valuesrelated to the amount or concentration of an analyte in a subject asmeasured using an analyte monitoring device, said method comprisingproviding a series of analyte-related signals obtained from the analytemonitoring device over time, wherein (a) two or more contiguousanalyte-related signals are used to obtain a single analyte measurementvalue (M), (b) analyte-related signals are not used to calculate morethan one analyte measurement value, and (c) said two or more contiguousanalyte-related signals, used to obtain the single analyte measurementvalue, comprise first and last analyte-related signals of the series;mathematically computing rolling analyte measurement values, wherein (i)each rolling analyte measurement value is calculated based on two ormore contiguous analyte-related signals from the series ofanalyte-related signals obtained from the analyte monitoring device,(ii) a subsequent rolling analyte measurement value is mathematicallycomputed by dropping said first analyte-related signal and including ananalyte-related signal contiguous and subsequent to the lastanalyte-related signal, (iii) further rolling analyte measurement valuesare obtained by repeating the dropping of the first analyte-relatedsignal used to calculate the previous rolling analyte measurement andincluding an analyte-related signal contiguous and subsequent to thelast analyte-related signal used to calculate the previous rollinganalyte measurement, and (iv) each rolling analyte measurement valueprovides a measurement related to the amount or concentration of analytein the subject; and increasing the number of analyte measurement valuesderived from the analyte-related signals in the series ofanalyte-related signals obtained from the analyte monitoring device byserially calculating rolling analyte measurement values, therebyincreasing the number of analyte measurement values relative to thenumber of analyte measurement values provided when two or morecontiguous analyte-related signals are used to obtain a single analytemeasurement value (M) and analyte-related signals are not used tocalculate more than one analyte measurement value.
 2. The method ofclaim 1, wherein said rolling analyte measurement value is an average oftwo or more analyte-related signals.
 3. The method of claim 1, whereinsaid rolling analyte measurement value is a sum of two or moreanalyte-related signals.
 4. The method of claim 1, wherein eachanalyte-related signal is represented by an integral over time, and saidrolling analyte measurement value is obtained by integral splitting. 5.The method of claim 1, wherein said monitoring device comprises asampling device and a sensing device, and wherein said providing theseries of analyte-related signals obtained from an analyte monitoringdevice comprises extracting a sample from the subject alternately into afirst collection reservoir and then into a second collection reservoirusing the sampling device, wherein (i) each sample comprises theanalyte, and (ii) said sampling device comprises said first and secondcollection reservoirs; and sensing the analyte in each extracted sampleto obtain a signal from each sample that is related to the analyteamount or concentration in the subject, thus providing a series ofanalyte-related signals, said sensing device comprising first and secondsensors, wherein said first sensor is in operative contact with saidfirst collection reservoir and said sensing provides signal S^(A) _(j)(where S_(A) is the signal from sensor A, j is the time interval), thesecond sensor is in operative contact with the second collectionreservoir and said sensing provides signal S^(B) _(j+1) (where S^(B) isthe signal from sensor B, j+1 is the time interval), and an analytemeasurement value is obtained using analyte-related signal from sensor Aand sensor B.
 6. The method of claim 5, wherein said rolling analytemeasurement values are calculated as follows:(average signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j))/2;  Eqn. 1(average signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1))2;  Eqn. 2(average signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2))/2; etc.,  Eqn. 3wherein (i) (j−1) is the measurement half-cycle previous to j, and (j+2)is two measurement half-cycles after j; and (ii) each average signalcorresponds to a rolling analyte measurement value.
 7. The method ofclaim 5, wherein said rolling analyte measurement values are calculatedas follows:(summed signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j));  Eqn. 4(summed signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1)); and  Eqn. 5(summed signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2)); etc.  Eqn. 6 where(j−1) is the measurement half-cycle previous to j, and (j+2) is twomeasurement half-cycles after j; and (ii) each summed signal correspondsto a rolling analyte measurement value.
 8. The method of claim 1,wherein a missing or error-associated signal in the series ofanalyte-related signals obtained from the analyte monitoring device isestimated using interpolation before mathematically computing rollinganalyte measurement values.
 9. The method of claim 1, wherein a missingor error-associated signal in the series of analyte-related signalsobtained from the analyte monitoring device is estimated usingextrapolation before mathematically computing rolling analytemeasurement values.
 10. The method of claim 1, wherein said analyte isglucose.
 11. The method of claim 1, wherein said analyte monitoringdevice comprises at least one sensing device.
 12. The method of claim 1,wherein said analyte-related signal is a current or a charge related toamount or concentration of analyte in the subject.
 13. The method ofclaim 1, wherein one or more microprocessors comprise programming tocontrol mathematical computation of rolling analyte measurement values.14. The method of claim 13, wherein said one or more microprocessorscomprise programming to control at least one component of the analytemonitoring device.
 15. The method of claim 14, wherein said analytemonitoring device comprises at least one sampling device and at leastone sensing device.
 16. The method of claim 15, wherein said one or moremicroprocessors control obtaining samples from the subject and sensinganalyte concentration in each obtained sample to provide the series ofanalyte-related signals.
 17. The method of claim 16, wherein saidanalyte monitoring device comprises (i) an iontophoretic samplingdevice, and (ii) an electrochemical sensing device.
 18. The method ofclaim 11, wherein said sensing device comprises a biosensor device. 19.The method of claim 18, wherein said biosensor device comprises anelectrode used in electrochemical detection of analyte.
 20. The methodof claim 11, wherein said analyte monitoring device further comprises atleast one sampling device.
 21. The method of claim 20, wherein saidsampling device employs a sampling method selected from the groupconsisting of iontophoresis, sonophoresis, microdialysis, suction, andpassive diffusion.
 22. One or more microprocessors comprisingprogramming to: control mathematical computation of rolling analytemeasurement values, wherein (i) each rolling analyte measurement valueis calculated based on two or more contiguous analyte-related signalsfrom a series of analyte-related signals obtained from an analytemonitoring device, (ii) said series of analyte-related signals isobtained from the analyte monitoring device over time, wherein (a) twoor more contiguous analyte-related signals are used to obtain a singleanalyte measurement value (M), (b) analyte-related signals are not usedto calculate more than one analyte measurement value, and (c) said twoor more contiguous analyte-related signals, used to obtain the singleanalyte measurement value, comprise first and last analyte-relatedsignals of the series, (iii) a subsequent rolling analyte measurementvalue is mathematically computed by dropping said first analyte-relatedsignal and including an analyte-related signal contiguous and subsequentto the last analyte-related signal, (iv) further rolling analytemeasurement values are obtained by repeating the dropping of the firstanalyte-related signal used to calculate the previous rolling analytemeasurement and including an analyte-related signal contiguous andsubsequent to the last analyte-related signal used to calculate theprevious rolling analyte measurement, and (v) each rolling analytemeasurement value provides a measurement related to the amount orconcentration of analyte in the subject; and increase the number ofanalyte measurement values derived from the analyte-related signals inthe series of analyte-related signals obtained from the analytemonitoring device by serially calculating rolling analyte measurementvalues, thereby increasing the number of analyte measurement valuesrelative to the number of analyte measurement values provided when twoor more contiguous analyte-related signals are used to obtain a singleanalyte measurement value (M) and analyte-related signals are not usedto calculate more than one analyte measurement value.
 23. The one ormore microprocessors of claim 22, wherein said analyte monitoring devicecomprises at least one sensing device and said one or moremicroprocessors are further programmed to control operation of saidsensing device.
 24. The one or more microprocessors of claim 23, whereinsaid analyte monitoring device further comprises at least one samplingdevice and said one or more microprocessors are further programmed tocontrol operation of said sampling device.
 25. The one or moremicroprocessors of claim 24, wherein said one or more microprocessorscontrol obtaining samples from the subject and sensing analyteconcentration in each obtained sample to provide the series ofanalyte-related signals.
 26. The one or more microprocessors of claim22, wherein said rolling analyte measurement value is an average of twoor more analyte-related signals.
 27. The one or more microprocessors ofclaim 22, wherein said rolling analyte measurement value is a sum of twoor more analyte-related signals.
 28. The one or more microprocessors ofclaim 22, wherein each analyte-related signal is represented by anintegral over time, and said rolling analyte measurement value isobtained by integral splitting.
 29. The one or more microprocessors ofclaim 22, wherein said monitoring device comprises a sampling device anda sensing device, and wherein said providing the series ofanalyte-related signals obtained from an analyte monitoring devicecomprises extracting a sample from the subject alternately into a firstcollection reservoir and then into a second collection reservoir usingthe sampling device, wherein (i) each sample comprises the analyte, and(ii) said sampling device comprises said first and second collectionreservoirs; and sensing the analyte in each extracted sample to obtain asignal from each sample that is related to the analyte amount orconcentration in the subject, thus providing a series of analyte-relatedsignals, said sensing device comprising first and second sensors,wherein said first sensor is in operative contact with said firstcollection reservoir and said sensing provides signal S^(A) _(j) (whereS^(A) is the signal from sensor A, j is the time interval), the secondsensor is in operative contact with the second collection reservoir andsaid sensing provides signal S^(B) _(j+1) (where S^(B) is the signalfrom sensor B, j+1 is the time interval), and an analyte measurementvalue is obtained using analyte-related signal from sensor A and sensorB.
 30. The one or more microprocessors of claim 29, wherein said rollinganalyte measurement values are calculated as follows:(average signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j))/2;  Eqn. 1(average signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1))/2;  Eqn. 2(average signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2))/2; etc.,  Eqn. 3wherein (i) (j−1) is the measurement half-cycle previous to j, and (j+2)is two measurement half-cycles after j, and (ii) each average signalcorresponds to a rolling analyte measurement value.
 31. The one or moremicroprocessors of claim 29, wherein said rolling analyte measurementvalues are calculated as follows:(summed signal)_(j)=(S ^(B) _(j−1) +S ^(A) _(j));  Eqn. 4(summed signal)_(j+1)=(S ^(A) _(j) +S ^(B) _(j+1)); and  Eqn. 5(summed signal)_(j+2)=(S ^(B) _(j+1) +S ^(A) _(j+2)); etc.  Eqn. 6 where(j−1) is the measurement half-cycle previous to j, and (j+2) is twomeasurement half-cycles after j; and (ii) each summed signal correspondsto a rolling analyte measurement value.
 32. The one or moremicroprocessors of claim 22, wherein a missing or error-associatedsignal in the series of analyte-related signals obtained from theanalyte monitoring device is estimated using interpolation beforemathematically computing rolling analyte measurement values.
 33. The oneor more microprocessors of claim 22, wherein a missing orerror-associated signal in the series of analyte-related signalsobtained from the analyte monitoring device is estimated usingextrapolation before mathematically computing rolling analytemeasurement values.
 34. The one or more microprocessors of claim 22,wherein said analyte is glucose.
 35. The one or more microprocessors ofclaim 34, wherein said analyte monitoring device comprises (i) aniontophoretic sampling device, and (ii) an electrochemical sensingdevice.
 36. The one or more microprocessors of claim 22, wherein saidanalyte-related signal is a current or a charge related to amount orconcentration of analyte in the subject.
 37. An analyte monitoringdevice comprising: a sensing device; and one or more microprocessorprogrammed to control operation of said sensing device, and controlmathematical computations of rolling analyte measurement values, wherein(i) each rolling analyte measurement value is calculated based on two ormore contiguous analyte-related signals from a series of analyte-relatedsignals obtained from an analyte monitoring device, (ii) said series ofanalyte-related signals is obtained from the analyte monitoring deviceover time, wherein (a) two or more contiguous analyte-related signalsare used to obtain a single analyte measurement value (M), (b)analyte-related signals are not used to calculate more than one analytemeasurement value, and (c) said two or more contiguous analyte-relatedsignals, used to obtain the single analyte measurement value, comprisefirst and last analyte-related signals of the series, (iii) a subsequentrolling analyte measurement value is mathematically computed by droppingsaid first analyte-related signal and including an analyte-relatedsignal contiguous and subsequent to the last analyte-related signal,(iv) further rolling analyte measurement values are obtained byrepeating the dropping of the first analyte-related signal used tocalculate the previous rolling analyte measurement and including ananalyte-related signal contiguous and subsequent to the lastanalyte-related signal used to calculate the previous rolling analytemeasurement, and (v) each rolling analyte measurement value provides ameasurement related to the amount or concentration of analyte in thesubject increasing the number of analyte measurement values derived fromthe analyte-related signals in the series of analyte-related signalsobtained from the analyte monitoring device by serially calculatingrolling analyte measurement values, thereby increasing the number ofanalyte measurement values relative to the number of analyte measurementvalues provided when two or more contiguous analyte-related signals areused to obtain a single analyte measurement value (M) andanalyte-related signals are not used to calculate more than one analytemeasurement value.
 38. The analyte monitoring device of claim 37,wherein said analyte monitoring device further comprises a samplingdevice.
 39. The analyte monitoring device of claim 38, wherein said oneor more microprocessors are further programmed to control the operationof said sampling device.
 40. The analyte monitoring device of claim 38,wherein said sampling device employs a sampling method selected from thegroup consisting of iontophoresis, sonophoresis, microdialysis, suction,and passive diffusion.
 41. The analyte monitoring device of claim 39,wherein said analyte monitoring device comprises (i) an iontophoreticsampling device, and (ii) an electrochemical sensing device.
 42. Theanalyte monitoring device of claim 37, wherein said sensing devicecomprise a biosensor device.
 43. The analyte monitoring device of claim42, wherein said biosensor device comprises an electrode used inelectrochemical detection of analyte.